Repository Analysis

PrimeIntellect-ai/prime-rl

Agentic RL Training at Scale

19.9 Moderate AI signal View on GitHub

Analysis Overview

This report presents the forensic synthetic code analysis of PrimeIntellect-ai/prime-rl, a Python project with 1,864 GitHub stars. SynthScan v2.0 examined 69,835 lines of code across 459 source files, recording 1001 pattern matches distributed across 18 syntactic categories. The overall adjusted score of 19.9 places this repository in the Moderate AI signal band.

The scanner applied 160+ deterministic lexical heuristics, multi-line block detectors, abstract syntax tree depth profilers, and a cross-file Jaccard similarity matrix to construct a statistically normalised synthetic code estimate. All matches are individually weighted by severity coefficient and contextual multiplier before summation, and the resulting headline score is temporally discounted to account for the repository's development history relative to the commercial emergence of large language model coding tooling (November 2022 onward).

19.9
Adjusted Score
19.9
Raw Score
100%
Time Factor
2026-08-09
Last Push
1.9K
Stars
Python
Language
69.8K
Lines of Code
459
Files
1.0K
Pattern Hits
2026-08-09
Scan Date
0.07
HC Hit Rate

What These Metrics Mean

Adjusted Score
Primary synthetic code indicator. Raw score normalised per 1,000 lines of code and multiplied by the temporal discount factor. This is the definitive comparative metric — use it to rank repositories by AI authorship density.
Raw Score
The unmodified sum of all severity-weighted, context-multiplied pattern match scores before temporal discounting. Reflects the absolute signal strength independent of when the repository was last active.
Time Factor
The temporal discount multiplier (0–100%) applied to the raw score. Repositories last updated before ChatGPT's launch (Nov 2022) receive a 5% factor. Full signal is only assigned to repositories active in the post-adoption era (Jan 2024+).
Pattern Hits
Total count of individual pattern matches across all files and categories. A high hit count with a low score may indicate a very large codebase with isolated AI snippets; a low count with a high score indicates dense, concentrated AI signatures.
HC Hit Rate
High+Critical pattern hits per file, averaged across the repository. This orthogonal signal catches repositories where a few files are densely packed with high-severity AI tells — a strong indicator even when the normalised score appears moderate due to codebase size.
Lines of Code / Files
Total lines and files analysed. The scanner examines 94 file extensions. These denominators are used to normalise the score, enabling fair comparison between repositories of vastly different sizes.

Score History

Longitudinal tracking requires multiple scan runs. Once this repository is re-scanned after new commits land, this chart will visualise how the synthetic code signal evolves over time — enabling you to detect whether AI authorship is growing, stabilising, or being actively corrected by human engineers.

No multi-scan history yet — run the scanner again to build trend data.

Severity Breakdown

Classifies detected patterns by their diagnostic confidence and structural impact. CRITICAL patterns (coefficient 10) represent definitive synthetic signatures — hallucinated imports, explicit LLM attribution metadata — virtually never produced by human authors. HIGH (5) indicates strong structural tells such as cross-file repetition or cross-linguistic idioms. MEDIUM (2) covers recognisable conversational padding and AI-specific vocabulary. LOW (1) captures subtle indicators like tautological comments and generic boilerplate that require density to carry independent signal.

CRITICAL 1HIGH 31MEDIUM 104LOW 865

Directory Score Breakdown

This horizontal bar chart decomposes the repository's raw synthetic code score by top-level directory, allowing you to pinpoint precisely which modules or components carry the highest AI authorship density. Directories with disproportionately high scores relative to their size warrant targeted manual review: concentrated AI signatures often trace back to mass-generated configuration layers, auto-ported test suites, LLM-scaffolded boilerplate classes, or entire subsystems authored under heavy copilot assistance. Use this view to prioritise your human code-review effort.

Pattern Findings

The scanner identified 1001 distinct pattern matches across 18 syntactic categories. Each entry below represents a discrete location in the source code where the engine recorded a statistically significant AI authorship indicator. Expand any category row to inspect the individual file paths, line numbers, code snippets, and the lexical context (CODE, COMMENT, or STRING) in which each match was detected.

Reading the findings table: The Severity column indicates the diagnostic confidence level (CRITICAL / HIGH / MEDIUM / LOW). The Context column identifies whether the match occurred inside executable code, an inline comment, or a string literal — comment-context matches receive a ×1.5 weight because LLMs systematically over-annotate. The ⚡ bolt icon marks clustered matches: three or more patterns within a 10-line window, each receiving an additional ×1.5 density multiplier as dense clusters constitute far stronger evidence of synthetic authorship than isolated hits.

Hyper-Verbose Identifiers476 hits · 492 pts
SeverityFileLineSnippetContext
LOWtests/utils.py24def check_number_goes_up_or_down(CODE
LOWtests/utils.py110def check_final_eval_reward_above(lines: list[str], env_name: str, min_threshold: float):CODE
LOWtests/utils.py145def check_avg_reward_in_range(CODE
LOWtests/utils.py178def check_avg_mismatch_kl_in_range(CODE
LOWtests/utils.py211def check_mismatch_kl_in_range(CODE
LOWtests/unit/test_configs.py98def test_toml_partial_nested_override(tmp_path):CODE
LOWtests/unit/test_configs.py107def test_toml_discriminated_union_default_type(tmp_path):CODE
LOWtests/unit/test_configs.py116def test_toml_discriminated_union_switch_variant(tmp_path):CODE
LOWtests/unit/test_configs.py124def test_toml_discriminated_union_override_switch_variant(tmp_path):CODE
LOWtests/unit/test_configs.py132def test_cli_overrides_defaults():CODE
LOWtests/unit/test_configs.py140def test_toml_overrides_defaults(tmp_path):CODE
LOWtests/unit/test_configs.py565def test_orchestrator_renderer_auto_rejects_unmapped_model():CODE
LOWtests/unit/test_configs.py571def test_orchestrator_renderer_auto_accepts_mapped_model():CODE
LOWtests/unit/test_configs.py578def test_sft_renderer_auto_accepts_prime_qwen_model():CODE
LOWtests/unit/test_configs.py583def test_sft_rejects_default_renderer_for_real_data():CODE
LOWtests/unit/test_configs.py588def test_sft_allows_unused_default_renderer_for_fake_data():CODE
LOWtests/unit/test_configs.py598def test_orchestrator_explicit_renderer_skips_unmapped_check():CODE
LOWtests/unit/test_configs.py159def test_removed_fused_lm_head_chunk_size_field_is_rejected():CODE
LOWtests/unit/test_configs.py164def test_to_toml_dict_roundtrips_explicit_none(tmp_path):CODE
LOWtests/unit/test_configs.py180def test_env_algo_overrides_top_level():CODE
LOWtests/unit/test_configs.py215def test_trainer_enable_token_export_cli_flag():CODE
LOWtests/unit/test_configs.py220def test_single_node_auto_inference_ports_follow_server_port():CODE
LOWtests/unit/test_configs.py241def test_multi_node_auto_inference_parallelism():CODE
LOWtests/unit/test_configs.py262def test_orchestrator_vlm_requires_renderer():CODE
LOWtests/unit/test_configs.py292def test_trainer_rejects_vlm_cp_with_ring():CODE
LOWtests/unit/test_configs.py310def test_selective_activation_checkpointing_requires_custom_impl():CODE
LOWtests/unit/test_configs.py315def test_shared_model_name_propagates_to_subconfigs():CODE
LOWtests/unit/test_configs.py332def test_shared_tokenizer_propagates_when_subconfigs_unset():CODE
LOWtests/unit/test_configs.py345def test_shared_and_sub_tokenizer_name_conflict_raises():CODE
LOWtests/unit/test_configs.py360def test_tokenizer_name_falls_back_to_model_name_when_unset():CODE
LOWtests/unit/test_configs.py375def test_explicit_subconfig_tokenizer_name_survives_shared_model_propagation():CODE
LOWtests/unit/test_configs.py408def test_tokenizer_chat_template_mismatch_raises():CODE
LOWtests/unit/test_configs.py418def test_shared_seq_len_propagates_to_subconfigs():CODE
LOWtests/unit/test_configs.py430def test_shared_and_sub_seq_len_conflict_raises():CODE
LOWtests/unit/test_configs.py444def test_shared_and_sub_model_name_conflict_raises():CODE
LOWtests/unit/test_configs.py456def test_shared_and_sub_max_steps_conflict_raises():CODE
LOWtests/unit/test_configs.py468def test_trainer_chat_template_cascades_to_inference():CODE
LOWtests/unit/test_configs.py488def test_shared_wandb_fields_propagate_to_subconfigs():CODE
LOWtests/unit/test_configs.py517def test_empty_shared_ckpt_block_does_not_conflict_with_subconfig_ckpt():CODE
LOWtests/unit/test_configs.py531def test_shared_and_subconfig_disjoint_fields_coexist():CODE
LOWtests/unit/test_configs.py545def test_shared_output_dir_propagates_through_cli(tmp_path):CODE
LOWtests/unit/test_configs.py610def test_orchestrator_renderer_none_rejected():CODE
LOWtests/unit/test_configs.py621def test_orchestrator_explicit_default_renderer_with_unmapped_model():CODE
LOWtests/unit/test_configs.py634def test_shared_model_name_resolves_inference_parsers():CODE
LOWtests/unit/test_configs.py652def test_explicit_inference_parser_wins_over_auto():CODE
LOWtests/unit/test_parsers.py97def test_resolve_tool_call_parser(model_name: str, expected_tool_call: str | None, expected_reasoning: str | None):CODE
LOWtests/unit/test_parsers.py102def test_resolve_reasoning_parser(model_name: str, expected_tool_call: str | None, expected_reasoning: str | None):CODE
LOWtests/unit/test_parsers.py106def test_inference_config_resolves_parsers_from_model_name():CODE
LOWtests/unit/test_parsers.py113def test_inference_config_explicit_parser_not_overridden():CODE
LOWtests/unit/test_parsers.py118def test_inference_config_none_disables_parser():CODE
LOWtests/unit/test_parsers.py126def test_to_namespace_resolves_parsers():CODE
LOWtests/unit/test_parsers.py134def test_to_namespace_none_strips_parser_attrs_from_namespace():CODE
LOWtests/unit/test_parsers.py145def test_to_namespace_unknown_model_disables_auto_tool_choice():CODE
LOWtests/unit/test_parsers.py152def test_to_namespace_passes_through_unknown_args():CODE
LOWtests/unit/utils/test_sequence.py19def test_get_cu_seqlens_from_position_ids_is_local_relative(CODE
LOWtests/unit/utils/test_sequence.py31def test_get_cu_seqlens_from_seq_lens():CODE
LOWtests/unit/utils/test_sequence.py39def test_get_cu_seqlens_from_seq_lens_rejects_wrong_total():CODE
LOWtests/unit/utils/test_metrics_server.py21def test_config_default_values():CODE
LOWtests/unit/utils/test_metrics_server.py32def test_config_invalid_port_low():CODE
LOWtests/unit/utils/test_metrics_server.py37def test_config_invalid_port_high():CODE
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Decorative Section Separators76 hits · 258 pts
SeverityFileLineSnippetContext
MEDIUMtests/unit/orchestrator/test_advantage.py162# --------------------------------------------------------------------------COMMENT
MEDIUMtests/unit/orchestrator/test_advantage.py164# --------------------------------------------------------------------------COMMENT
MEDIUMtests/unit/orchestrator/test_advantage.py187# --------------------------------------------------------------------------COMMENT
MEDIUMtests/unit/orchestrator/test_advantage.py189# --------------------------------------------------------------------------COMMENT
MEDIUMtests/unit/orchestrator/test_advantage.py237# --------------------------------------------------------------------------COMMENT
MEDIUMtests/unit/orchestrator/test_advantage.py239# --------------------------------------------------------------------------COMMENT
MEDIUMtests/unit/orchestrator/test_qwen3_vl_e2e.py102 # ── Build a real Qwen3VLRenderer with a real processor. ─────────────COMMENT
MEDIUMtests/unit/orchestrator/test_qwen3_vl_e2e.py109 # ── Manually wire a RendererClient bypassing the pool factory. ──────COMMENT
MEDIUMtests/unit/orchestrator/test_qwen3_vl_e2e.py118 # ── Build a verifiers-shaped user message with an image. ────────────COMMENT
MEDIUMtests/unit/orchestrator/test_qwen3_vl_e2e.py144 # ── The HTTP body should carry a features payload. ──────────────────COMMENT
MEDIUMtests/unit/orchestrator/test_qwen3_vl_e2e.py152 # ── Pydantic-roundtrip through vLLM's GenerateRequest model. ────────COMMENT
MEDIUMtests/unit/orchestrator/test_qwen3_vl_e2e.py163 # exactly on a run of <|image_pad|> ids in the prompt. ───────────COMMENT
MEDIUMtests/unit/orchestrator/test_qwen3_vl_e2e.py186 # ── Response parsed through renderer's parse_response. ──────────────COMMENT
MEDIUMtests/unit/orchestrator/test_algorithms.py91# --------------------------------------------------------------------------COMMENT
MEDIUMtests/unit/orchestrator/test_algorithms.py97# --------------------------------------------------------------------------COMMENT
MEDIUMtests/unit/orchestrator/test_algorithms.py216# --------------------------------------------------------------------------COMMENT
MEDIUMtests/unit/orchestrator/test_algorithms.py223# --------------------------------------------------------------------------COMMENT
MEDIUMtests/integration/test_benchmark_regression.py166# =============================================================================COMMENT
MEDIUMtests/integration/test_benchmark_regression.py168# =============================================================================COMMENT
MEDIUMtests/integration/test_benchmark_regression.py226# =============================================================================COMMENT
MEDIUMtests/integration/test_benchmark_regression.py228# =============================================================================COMMENT
MEDIUMscripts/install_ep_kernels.sh49# ── Auto-detect CUDA toolkit matching torch ───────────────────────────────────COMMENT
MEDIUMscripts/install_ep_kernels.sh66# ── Auto-detect GPU architecture ──────────────────────────────────────────────COMMENT
MEDIUMscripts/install_ep_kernels.sh91# ── Step 1: Install build deps ────────────────────────────────────────────────COMMENT
MEDIUMscripts/install_ep_kernels.sh97# ── Step 2: Download and extract NVSHMEM ──────────────────────────────────────COMMENT
MEDIUMscripts/install_ep_kernels.sh127# ── Step 3: Build and install DeepEP ──────────────────────────────────────────COMMENT
MEDIUMscripts/install_ep_kernels.sh148# ── Step 4 (optional): Configure IBGDA drivers ───────────────────────────────COMMENT
MEDIUM…ges/prime-rl-configs/src/prime_rl/configs/algorithm.py77# ---------------------------------------------------------------------------COMMENT
MEDIUM…ges/prime-rl-configs/src/prime_rl/configs/algorithm.py79# ---------------------------------------------------------------------------COMMENT
MEDIUM…ges/prime-rl-configs/src/prime_rl/configs/algorithm.py90# ---------------------------------------------------------------------------COMMENT
MEDIUM…ges/prime-rl-configs/src/prime_rl/configs/algorithm.py92# ---------------------------------------------------------------------------COMMENT
MEDIUM…ges/prime-rl-configs/src/prime_rl/configs/algorithm.py155# ---------------------------------------------------------------------------COMMENT
MEDIUM…ges/prime-rl-configs/src/prime_rl/configs/algorithm.py157# ---------------------------------------------------------------------------COMMENT
MEDIUMsrc/prime_rl/_compat.py7# ---------------------------------------------------------------------------COMMENT
MEDIUMsrc/prime_rl/_compat.py15# ---------------------------------------------------------------------------COMMENT
MEDIUMsrc/prime_rl/_compat.py22# ---------------------------------------------------------------------------COMMENT
MEDIUMsrc/prime_rl/_compat.py35# ---------------------------------------------------------------------------COMMENT
MEDIUMsrc/prime_rl/utils/vlm.py39# ---------------------------------------------------------------------------COMMENT
MEDIUMsrc/prime_rl/utils/vlm.py41# ---------------------------------------------------------------------------COMMENT
MEDIUMsrc/prime_rl/utils/vlm.py114# ---------------------------------------------------------------------------COMMENT
MEDIUMsrc/prime_rl/utils/vlm.py116# ---------------------------------------------------------------------------COMMENT
MEDIUMsrc/prime_rl/trainer/models/kernels/fp8_utils.py25# ---------------------------------------------------------------------------COMMENT
MEDIUMsrc/prime_rl/trainer/models/kernels/fp8_utils.py27# ---------------------------------------------------------------------------COMMENT
MEDIUMsrc/prime_rl/trainer/models/kernels/fp8_utils.py90# ---------------------------------------------------------------------------COMMENT
MEDIUMsrc/prime_rl/trainer/models/kernels/fp8_utils.py92# ---------------------------------------------------------------------------COMMENT
MEDIUMsrc/prime_rl/trainer/models/kernels/fp8_utils.py370# ---------------------------------------------------------------------------COMMENT
MEDIUMsrc/prime_rl/trainer/models/kernels/fp8_utils.py372# ---------------------------------------------------------------------------COMMENT
MEDIUMsrc/prime_rl/trainer/models/layers/ring_attn.py314# ---------------------------------------------------------------------------COMMENT
MEDIUMsrc/prime_rl/trainer/models/layers/ring_attn.py316# ---------------------------------------------------------------------------COMMENT
MEDIUM…_rl/trainer/models/qwen3_5_moe/modeling_qwen3_5_moe.py924 # ------------------------------------------------------------------COMMENT
MEDIUM…_rl/trainer/models/qwen3_5_moe/modeling_qwen3_5_moe.py926 # ------------------------------------------------------------------COMMENT
MEDIUM…_rl/trainer/models/qwen3_5_moe/modeling_qwen3_5_moe.py59# ---------------------------------------------------------------------------COMMENT
MEDIUM…_rl/trainer/models/qwen3_5_moe/modeling_qwen3_5_moe.py61# ---------------------------------------------------------------------------COMMENT
MEDIUM…_rl/trainer/models/qwen3_5_moe/modeling_qwen3_5_moe.py81# ---------------------------------------------------------------------------COMMENT
MEDIUM…_rl/trainer/models/qwen3_5_moe/modeling_qwen3_5_moe.py83# ---------------------------------------------------------------------------COMMENT
MEDIUM…_rl/trainer/models/qwen3_5_moe/modeling_qwen3_5_moe.py230# ---------------------------------------------------------------------------COMMENT
MEDIUM…_rl/trainer/models/qwen3_5_moe/modeling_qwen3_5_moe.py232# ---------------------------------------------------------------------------COMMENT
MEDIUM…_rl/trainer/models/qwen3_5_moe/modeling_qwen3_5_moe.py395# ---------------------------------------------------------------------------COMMENT
MEDIUM…_rl/trainer/models/qwen3_5_moe/modeling_qwen3_5_moe.py397# ---------------------------------------------------------------------------COMMENT
MEDIUM…_rl/trainer/models/qwen3_5_moe/modeling_qwen3_5_moe.py504# ---------------------------------------------------------------------------COMMENT
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Unused Imports165 hits · 165 pts
SeverityFileLineSnippetContext
LOWtests/unit/train/test_model.py24CODE
LOWtests/unit/inference/test_serving_tokens.py11CODE
LOWtests/unit/orchestrator/test_qwen3_vl_e2e.py14CODE
LOWtests/integration/test_benchmark_regression.py9CODE
LOWbenchmarks/scripts/aggregate_results.py4CODE
LOWbenchmarks/scripts/run_single_benchmark.py9CODE
LOWpackages/prime-rl-configs/src/prime_rl/utils/config.py5CODE
LOWpackages/prime-rl-configs/src/prime_rl/utils/config.py6CODE
LOW…ages/prime-rl-configs/src/prime_rl/utils/validation.py1CODE
LOWpackages/prime-rl-configs/src/prime_rl/configs/rl.py21CODE
LOWsrc/prime_rl/transport/__init__.py9CODE
LOWsrc/prime_rl/transport/__init__.py9CODE
LOWsrc/prime_rl/transport/__init__.py9CODE
LOWsrc/prime_rl/utils/async_utils.py1CODE
LOWsrc/prime_rl/utils/sequence.py1CODE
LOWsrc/prime_rl/utils/client.py1CODE
LOWsrc/prime_rl/utils/cp.py1CODE
LOWsrc/prime_rl/utils/cp.py4CODE
LOWsrc/prime_rl/utils/utils.py4CODE
LOWsrc/prime_rl/utils/utils.py19CODE
LOWsrc/prime_rl/utils/utils.py19CODE
LOWsrc/prime_rl/utils/utils.py19CODE
LOWsrc/prime_rl/utils/utils.py19CODE
LOWsrc/prime_rl/utils/utils.py19CODE
LOWsrc/prime_rl/utils/utils.py19CODE
LOWsrc/prime_rl/utils/utils.py19CODE
LOWsrc/prime_rl/utils/utils.py19CODE
LOWsrc/prime_rl/utils/utils.py19CODE
LOWsrc/prime_rl/utils/utils.py19CODE
LOWsrc/prime_rl/utils/utils.py19CODE
LOWsrc/prime_rl/utils/metrics_server.py17CODE
LOWsrc/prime_rl/utils/monitor/prime.py1CODE
LOWsrc/prime_rl/utils/monitor/wandb.py1CODE
LOWsrc/prime_rl/utils/monitor/multi.py1CODE
LOWsrc/prime_rl/utils/monitor/file.py1CODE
LOWsrc/prime_rl/utils/monitor/base.py1CODE
LOWsrc/prime_rl/inference/vllm/server.py200CODE
LOWsrc/prime_rl/inference/vllm/routed_experts.py1CODE
LOWsrc/prime_rl/inference/vllm/serving_tokens.py27CODE
LOWsrc/prime_rl/inference/vllm/worker/nixl.py3CODE
LOWsrc/prime_rl/trainer/scheduler.py1CODE
LOWsrc/prime_rl/trainer/distributed/__init__.py1CODE
LOWsrc/prime_rl/trainer/distributed/__init__.py1CODE
LOWsrc/prime_rl/trainer/distributed/__init__.py1CODE
LOWsrc/prime_rl/trainer/models/conversion_ops.py36CODE
LOWsrc/prime_rl/trainer/models/__init__.py19CODE
LOWsrc/prime_rl/trainer/models/__init__.py19CODE
LOWsrc/prime_rl/trainer/models/laguna/__init__.py1CODE
LOWsrc/prime_rl/trainer/models/laguna/__init__.py2CODE
LOWsrc/prime_rl/trainer/models/laguna/__init__.py2CODE
LOWsrc/prime_rl/trainer/models/laguna/__init__.py2CODE
LOWsrc/prime_rl/trainer/models/laguna/converting_laguna.py20CODE
LOWsrc/prime_rl/trainer/models/kernels/fp8_utils.py1CODE
LOWsrc/prime_rl/trainer/models/gpt_oss/__init__.py1CODE
LOWsrc/prime_rl/trainer/models/gpt_oss/__init__.py2CODE
LOWsrc/prime_rl/trainer/models/gpt_oss/__init__.py2CODE
LOWsrc/prime_rl/trainer/models/gpt_oss/__init__.py2CODE
LOW…ime_rl/trainer/models/gpt_oss/configuration_gpt_oss.py4CODE
LOWsrc/prime_rl/trainer/models/layers/lm_head_gemma.py1CODE
LOWsrc/prime_rl/trainer/models/layers/mxfp8_linear.py1CODE
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Cross-File Repetition28 hits · 140 pts
SeverityFileLineSnippetContext
HIGHpackages/prime-rl-configs/src/prime_rl/configs/rl.py0keep at most this many recent step checkpoints on disk. if none, never clean old checkpoints based on recency.STRING
HIGH…/prime-rl-configs/src/prime_rl/configs/orchestrator.py0keep at most this many recent step checkpoints on disk. if none, never clean old checkpoints based on recency.STRING
HIGH…kages/prime-rl-configs/src/prime_rl/configs/trainer.py0keep at most this many recent step checkpoints on disk. if none, never clean old checkpoints based on recency.STRING
HIGHpackages/prime-rl-configs/src/prime_rl/configs/rl.py0keep checkpoints at every n steps permanently (e.g. ``keep_interval=100`` keeps step 100, 200, ...). if none, no intervaSTRING
HIGH…/prime-rl-configs/src/prime_rl/configs/orchestrator.py0keep checkpoints at every n steps permanently (e.g. ``keep_interval=100`` keeps step 100, 200, ...). if none, no intervaSTRING
HIGH…kages/prime-rl-configs/src/prime_rl/configs/trainer.py0keep checkpoints at every n steps permanently (e.g. ``keep_interval=100`` keeps step 100, 200, ...). if none, no intervaSTRING
HIGHpackages/prime-rl-configs/src/prime_rl/configs/rl.py0only validate and dump resolved configs, then exit early.STRING
HIGH…ges/prime-rl-configs/src/prime_rl/configs/inference.py0only validate and dump resolved configs, then exit early.STRING
HIGHpackages/prime-rl-configs/src/prime_rl/configs/sft.py0only validate and dump resolved configs, then exit early.STRING
HIGH…/prime-rl-configs/src/prime_rl/configs/orchestrator.py0extra environment variables for the sft trainer process(es). merged on top of the launcher defaults.STRING
HIGH…kages/prime-rl-configs/src/prime_rl/configs/trainer.py0extra environment variables for the sft trainer process(es). merged on top of the launcher defaults.STRING
HIGHpackages/prime-rl-configs/src/prime_rl/configs/sft.py0extra environment variables for the sft trainer process(es). merged on top of the launcher defaults.STRING
HIGH…/prime-rl-configs/src/prime_rl/configs/orchestrator.py0local jsonl metric sink. if set, metrics are appended to ``<output_dir>/metrics.jsonl``.STRING
HIGH…kages/prime-rl-configs/src/prime_rl/configs/trainer.py0local jsonl metric sink. if set, metrics are appended to ``<output_dir>/metrics.jsonl``.STRING
HIGHpackages/prime-rl-configs/src/prime_rl/configs/sft.py0local jsonl metric sink. if set, metrics are appended to ``<output_dir>/metrics.jsonl``.STRING
HIGH…/prime-rl-configs/src/prime_rl/configs/orchestrator.py0directory to write outputs to — checkpoints and logs are written as subdirectories. should be a persistent directory witSTRING
HIGH…kages/prime-rl-configs/src/prime_rl/configs/trainer.py0directory to write outputs to — checkpoints and logs are written as subdirectories. should be a persistent directory witSTRING
HIGHpackages/prime-rl-configs/src/prime_rl/configs/sft.py0directory to write outputs to — checkpoints and logs are written as subdirectories. should be a persistent directory witSTRING
HIGH…/prime-rl-configs/src/prime_rl/configs/orchestrator.py0betterstack heartbeat configuration for monitoring training progress.STRING
HIGH…kages/prime-rl-configs/src/prime_rl/configs/trainer.py0betterstack heartbeat configuration for monitoring training progress.STRING
HIGHpackages/prime-rl-configs/src/prime_rl/configs/sft.py0betterstack heartbeat configuration for monitoring training progress.STRING
HIGHsrc/prime_rl/trainer/models/layers/attn.py0run the flash attention kernel. q/k/v are [total_tokens, heads, dim].STRING
HIGH…_rl/trainer/models/qwen3_5_moe/modeling_qwen3_5_moe.py0run the flash attention kernel. q/k/v are [total_tokens, heads, dim].STRING
HIGHsrc/prime_rl/trainer/models/afmoe/modeling_afmoe.py0run the flash attention kernel. q/k/v are [total_tokens, heads, dim].STRING
HIGH…me_rl/trainer/models/minimax_m2/modeling_minimax_m2.py0routed_experts (`torch.longtensor` of shape `(batch_size, sequence_length, num_hidden_layers, num_experts_per_tok)`, *opSTRING
HIGH…/prime_rl/trainer/models/glm4_moe/modeling_glm4_moe.py0routed_experts (`torch.longtensor` of shape `(batch_size, sequence_length, num_hidden_layers, num_experts_per_tok)`, *opSTRING
HIGH…rime_rl/trainer/models/qwen3_moe/modeling_qwen3_moe.py0routed_experts (`torch.longtensor` of shape `(batch_size, sequence_length, num_hidden_layers, num_experts_per_tok)`, *opSTRING
HIGH…_rl/trainer/models/glm_moe_dsa/modeling_glm_moe_dsa.py0routed_experts (`torch.longtensor` of shape `(batch_size, sequence_length, num_hidden_layers, num_experts_per_tok)`, *opSTRING
Modern Structural Boilerplate76 hits · 73 pts
SeverityFileLineSnippetContext
LOW…/train/models/afmoe_hf_modeling/configuration_afmoe.py132__all__ = ["AfmoeConfig"]CODE
LOW…/unit/train/models/afmoe_hf_modeling/modeling_afmoe.py641__all__ = [CODE
LOWsrc/prime_rl/transport/__init__.py42__all__ = [CODE
LOWsrc/prime_rl/utils/client.py79 def update_model_name(self, model_name: str) -> None:CODE
LOWsrc/prime_rl/utils/client.py89 async def update_weights(self, weight_dir: Path | None, lora_name: str | None = None, step: int = 0) -> None:CODE
LOWsrc/prime_rl/utils/process.py36def set_proc_title(name: str) -> None:CODE
LOWsrc/prime_rl/utils/monitor/__init__.py13__all__ = [CODE
LOWsrc/prime_rl/inference/vllm/worker/nccl.py138 def update_weights_from_path(self, weight_dir: str) -> None:CODE
LOWsrc/prime_rl/inference/vllm/worker/filesystem.py28 def update_weights_from_path(self, weight_path: str) -> None:CODE
LOWsrc/prime_rl/inference/vllm/worker/__init__.py11logger = logging.getLogger(__name__)CODE
LOWsrc/prime_rl/inference/vllm/worker/weight_transfer.py138def update_mla_absorbed_weights(model: Module) -> None:CODE
LOWsrc/prime_rl/inference/vllm/worker/nixl.py507 def update_weights_from_path(self, weight_dir: str | None = None) -> None:CODE
LOWsrc/prime_rl/trainer/parallel_dims.py32__all__ = ["ParallelDims"]CODE
LOWsrc/prime_rl/trainer/lora.py124def _set_module_by_name(model: nn.Module, module_name: str, new_module: nn.Module) -> None:CODE
LOWsrc/prime_rl/trainer/distributed/deepep.py338__all__ = [CODE
LOWsrc/prime_rl/trainer/distributed/__init__.py3__all__ = ["DeepEPExpertParallel", "MXFP8AllToAllExpertParallel", "get_ep_group"]CODE
LOWsrc/prime_rl/trainer/models/__init__.py104__all__ = [CODE
LOWsrc/prime_rl/trainer/models/base.py148__all__ = ["PreTrainedModelPrimeRL"]CODE
LOWsrc/prime_rl/trainer/models/laguna/__init__.py8__all__ = [CODE
LOW…prime_rl/trainer/models/laguna/configuration_laguna.py193__all__ = ["LagunaConfig"]CODE
LOWsrc/prime_rl/trainer/models/laguna/modeling_laguna.py395__all__ = [CODE
LOWsrc/prime_rl/trainer/models/gpt_oss/modeling_gpt_oss.py356__all__ = [STRING
LOWsrc/prime_rl/trainer/models/gpt_oss/__init__.py8__all__ = [CODE
LOW…ime_rl/trainer/models/gpt_oss/configuration_gpt_oss.py6__all__ = ["GptOssConfig"]CODE
LOWsrc/prime_rl/trainer/models/layers/ring_attn.py11def _set_fa3_signature_params(params: dict, causal: bool, window_size: tuple[int, int]) -> None:CODE
LOWsrc/prime_rl/trainer/models/layers/moe.py621 def set_ep_comm_backend(self, backend: EPCommBackend) -> None:CODE
LOWsrc/prime_rl/trainer/models/layers/moe.py625 def set_deepep_token_chunk_size(self, chunk_size: int | None) -> None:CODE
LOWsrc/prime_rl/trainer/models/layers/moe.py1106 def set_ep_comm_backend(self, backend: EPCommBackend) -> None:CODE
LOWsrc/prime_rl/trainer/models/layers/moe.py1110 def set_deepep_token_chunk_size(self, chunk_size: int | None) -> None:CODE
LOWsrc/prime_rl/trainer/models/layers/moe.py202 def set_ep_comm_backend(self, backend: EPCommBackend) -> None:CODE
LOWsrc/prime_rl/trainer/models/layers/moe.py354 def set_ep_comm_backend(self, backend: EPCommBackend) -> None:CODE
LOWsrc/prime_rl/trainer/models/layers/moe.py905 def set_ep_comm_backend(self, backend: EPCommBackend) -> None:CODE
LOWsrc/prime_rl/trainer/models/layers/ulysses_attn.py48def update_ulysses_params(cu_seqlens: torch.Tensor, max_seqlen: int) -> None:CODE
LOWsrc/prime_rl/trainer/models/layers/rms_norm.py3__all__ = ["RMSNorm", "RMSNormConfig"]CODE
LOWsrc/prime_rl/trainer/models/layers/checkpointing.py120def set_selective_activation_checkpointing(layer: nn.Module, targets: Iterable[str]) -> None:CODE
LOWsrc/prime_rl/trainer/models/layers/lora/__init__.py11__all__ = [CODE
LOWsrc/prime_rl/trainer/models/layers/lora/base.py14def set_lora_num_tokens(num_tokens: torch.Tensor, reset_reference: bool = False) -> None:CODE
LOWsrc/prime_rl/trainer/models/layers/lora/base.py42def set_multilora_scaling(scaling_factors: torch.Tensor, reset_reference: bool = False) -> None:CODE
LOWsrc/prime_rl/trainer/models/minimax_m2/__init__.py8__all__ = [CODE
LOW…me_rl/trainer/models/minimax_m2/modeling_minimax_m2.py275__all__ = [STRING
LOW…/trainer/models/nemotron_h/configuration_nemotron_h.py227__all__ = ["NemotronHConfig"]CODE
LOWsrc/prime_rl/trainer/models/nemotron_h/__init__.py8__all__ = [CODE
LOW…me_rl/trainer/models/nemotron_h/modeling_nemotron_h.py191 def set_context_parallel_attributes(self, cp_group: dist.ProcessGroup, cp_rank: int, cp_world_size: int) -> None:CODE
LOW…me_rl/trainer/models/nemotron_h/modeling_nemotron_h.py407 def set_context_parallel_attributes(self, cp_group: dist.ProcessGroup, cp_rank: int, cp_world_size: int) -> None:CODE
LOW…me_rl/trainer/models/nemotron_h/modeling_nemotron_h.py477 def set_context_parallel_attributes(self, cp_group: dist.ProcessGroup, cp_rank: int, cp_world_size: int) -> None:CODE
LOW…me_rl/trainer/models/nemotron_h/modeling_nemotron_h.py521__all__ = [CODE
LOWsrc/prime_rl/trainer/models/qwen3/__init__.py3__all__ = ["Qwen3ForCausalLM", "Qwen3Model", "Qwen3PreTrainedModel"]CODE
LOWsrc/prime_rl/trainer/models/qwen3/modeling_qwen3.py281__all__ = ["Qwen3ForCausalLM", "Qwen3Model", "Qwen3PreTrainedModel"]STRING
LOW…_rl/trainer/models/qwen3_5_moe/modeling_qwen3_5_moe.py921 def set_context_parallel_attributes(self, cp_group, cp_rank: int, cp_world_size: int) -> None:CODE
LOW…_rl/trainer/models/qwen3_5_moe/modeling_qwen3_5_moe.py685 def set_context_parallel_attributes(self, cp_group, cp_rank: int, cp_world_size: int) -> None:CODE
LOW…_rl/trainer/models/qwen3_5_moe/modeling_qwen3_5_moe.py773 def set_context_parallel_attributes(self, cp_group, cp_rank: int, cp_world_size: int) -> None:CODE
LOW…_rl/trainer/models/qwen3_5_moe/modeling_qwen3_5_moe.py1022__all__ = [CODE
LOWsrc/prime_rl/trainer/models/qwen3_5_moe/__init__.py8__all__ = [CODE
LOWsrc/prime_rl/trainer/models/glm_moe_dsa/__init__.py8__all__ = [CODE
LOW…_rl/trainer/models/glm_moe_dsa/modeling_glm_moe_dsa.py84 def set_context_parallel_attributes(self, cp_group: dist.ProcessGroup, cp_rank: int, cp_world_size: int) -> None:CODE
LOW…_rl/trainer/models/glm_moe_dsa/modeling_glm_moe_dsa.py361__all__ = ["GlmMoeDsaConfig", "GlmMoeDsaPreTrainedModel", "GlmMoeDsaModel", "GlmMoeDsaForCausalLM"]STRING
LOW…_rl/trainer/models/glm_moe_dsa/sparse_mla_attention.py171 def set_context_parallel_attributes(self, cp_group: dist.ProcessGroup, cp_rank: int, cp_world_size: int) -> None:CODE
LOW…c/prime_rl/trainer/models/afmoe/configuration_afmoe.py116__all__ = ["AfmoeConfig"]CODE
LOWsrc/prime_rl/trainer/models/afmoe/__init__.py8__all__ = [CODE
LOWsrc/prime_rl/trainer/models/afmoe/modeling_afmoe.py507__all__ = [STRING
16 more matches not shown…
Excessive Try-Catch Wrapping41 hits · 47 pts
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LOWbenchmarks/scripts/aggregate_results.py76 except Exception as e:CODE
LOWbenchmarks/scripts/run_single_benchmark.py32 except Exception:CODE
LOWbenchmarks/scripts/run_single_benchmark.py238 except Exception as e:CODE
MEDIUMbenchmarks/scripts/run_single_benchmark.py241 print(f"Error: {e}")CODE
LOWscripts/chat.py161 except Exception as e:CODE
LOWsrc/prime_rl/transport/zmq.py59 except Exception:CODE
LOWsrc/prime_rl/utils/client.py191 except Exception as e:CODE
LOWsrc/prime_rl/utils/utils.py54 except Exception:CODE
LOWsrc/prime_rl/utils/utils.py74 except Exception:CODE
MEDIUMsrc/prime_rl/utils/utils.py49def async_wrapper(*args, **kwargs):CODE
MEDIUMsrc/prime_rl/utils/utils.py69def sync_wrapper(*args, **kwargs):CODE
LOWsrc/prime_rl/utils/background_async.py61 except Exception as e:CODE
LOWsrc/prime_rl/utils/background_async.py95 except Exception as e:CODE
LOWsrc/prime_rl/utils/usage_reporter.py66 except Exception as e:CODE
LOWsrc/prime_rl/utils/monitor/prime.py388 except Exception as e:CODE
LOWsrc/prime_rl/utils/monitor/prime.py407 except Exception as e:CODE
LOWsrc/prime_rl/utils/monitor/prime.py420 except Exception as e:CODE
LOWsrc/prime_rl/utils/monitor/prime.py439 except Exception as e:CODE
LOWsrc/prime_rl/utils/monitor/prime.py555 except Exception as e:CODE
LOWsrc/prime_rl/utils/monitor/prime.py597 except Exception as e:CODE
LOWsrc/prime_rl/utils/monitor/prime.py616 except Exception as e:CODE
LOWsrc/prime_rl/utils/monitor/wandb.py155 except Exception as e:CODE
LOWsrc/prime_rl/utils/monitor/wandb.py454 except Exception as e:CODE
LOWsrc/prime_rl/utils/monitor/multi.py33 except Exception as e:CODE
LOWsrc/prime_rl/utils/monitor/multi.py40 except Exception as e:CODE
LOWsrc/prime_rl/utils/monitor/multi.py47 except Exception as e:CODE
LOWsrc/prime_rl/utils/monitor/multi.py54 except Exception as e:CODE
LOWsrc/prime_rl/utils/monitor/multi.py61 except Exception as e:CODE
LOWsrc/prime_rl/utils/monitor/multi.py68 except Exception as e:CODE
LOWsrc/prime_rl/entrypoints/rl.py424 except Exception as e:CODE
LOWsrc/prime_rl/entrypoints/sft.py201 except Exception as e:CODE
LOWsrc/prime_rl/inference/vllm/serving_tokens.py142 except Exception:CODE
LOWsrc/prime_rl/trainer/models/kernels/sparse_mla_bwd.py12except Exception:CODE
LOWsrc/prime_rl/trainer/models/kernels/sparse_mla_fwd.py12except Exception:CODE
LOWsrc/prime_rl/orchestrator/dispatcher.py509 except Exception as exc:CODE
LOWsrc/prime_rl/orchestrator/inference_metrics.py407 except Exception as e:CODE
LOWsrc/prime_rl/orchestrator/inference_metrics.py421 except Exception as e:CODE
LOWsrc/prime_rl/orchestrator/utils.py92 except Exception as exc:CODE
LOWsrc/prime_rl/orchestrator/packing.py20 except Exception as e:CODE
LOWsrc/prime_rl/orchestrator/watcher.py132 except Exception as exc:CODE
LOWsrc/prime_rl/orchestrator/watcher.py151 except Exception as exc:CODE
AI Slop Vocabulary14 hits · 40 pts
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MEDIUMexamples/advanced/glm-4.5-air/terminal.toml2# run through the rlm harness (ipython-only) on a prime sandbox. Each task ships its own Docker imageCOMMENT
MEDIUMexamples/advanced/glm-4.5-air/terminal.toml16# Clean up this run's orphaned prime sandboxes before launch (hardcoded label, matches the harness labels below).COMMENT
MEDIUMexamples/advanced/glm-4.5-air/terminal.toml110# # Metric-only monitors (weight 0): rlm harness-mechanics + swe code-usage rubrics, judged by GLM-5.2.COMMENT
MEDIUMexamples/advanced/glm-4.5-air/swe.toml1# v1 RL: GLM-4.5-Air (100B MoE) on the v1 `scaleswe-v1` SWE taskset, rlm harness on a prime sandboxCOMMENT
MEDIUMexamples/advanced/glm-4.5-air/swe.toml26# Clean up this run's orphaned prime sandboxes before launch (hardcoded label, matches the harness labels below).COMMENT
MEDIUMexamples/advanced/glm-4.5-air/swe.toml118# --- Eval: SWE-Bench Verified at step 0 + every 20 steps, rlm harness on prime ---COMMENT
MEDIUMexamples/advanced/glm-4.5-air/search.toml2# through the rlm harness (its built-in `search` skill) on a prime sandbox. GRPO + linear lengthCOMMENT
MEDIUMexamples/advanced/glm-4.5-air/search.toml15# Clean up this run's orphaned prime sandboxes before launch (hardcoded label, matches the harness labels below).COMMENT
MEDIUMexamples/advanced/glm-4.5-air/search.toml121# # Metric-only monitor (weight 0): rlm harness-mechanics rubric, judged by GLM-5.2.COMMENT
MEDIUMexamples/advanced/glm-4.5-air/search.toml154# # Metric-only monitor (weight 0): rlm harness-mechanics rubric, judged by GLM-5.2.COMMENT
MEDIUMexamples/advanced/glm-4.5-air/search.toml192# # Metric-only monitor (weight 0): rlm harness-mechanics rubric, judged by GLM-5.2.COMMENT
MEDIUMsrc/prime_rl/inference/server.py10 # spawn is more robust in vLLM nightlies and Qwen3-VL (fork can deadlock with multithreaded processes)COMMENT
LOWsrc/prime_rl/trainer/perf.py157 # Some MoE models (e.g. DeepSeek) use moe_intermediate_size, others (e.g. Granite) just use intermediate_sizeCOMMENT
MEDIUMsrc/prime_rl/trainer/sft/data.py295 # scaffolding (e.g. <|im_start|>assistant) the harness emits.COMMENT
Deep Nesting40 hits · 40 pts
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LOWscripts/chat.py15CODE
LOWpackages/prime-rl-configs/src/prime_rl/configs/rl.py605CODE
LOWsrc/prime_rl/utils/act_offloading.py56CODE
LOWsrc/prime_rl/utils/act_offloading.py116CODE
LOWsrc/prime_rl/utils/nccl.py8CODE
LOWsrc/prime_rl/utils/chat_template.py5CODE
LOWsrc/prime_rl/utils/utils.py110CODE
LOWsrc/prime_rl/utils/usage_reporter.py49CODE
LOWsrc/prime_rl/entrypoints/rl.py124CODE
LOWsrc/prime_rl/inference/patches.py314CODE
LOWsrc/prime_rl/inference/patches.py339CODE
LOWsrc/prime_rl/inference/vllm/worker/weight_transfer.py98CODE
LOWsrc/prime_rl/inference/vllm/worker/nixl.py160CODE
LOWsrc/prime_rl/inference/vllm/worker/nixl.py529CODE
LOWsrc/prime_rl/trainer/batch.py213CODE
LOWsrc/prime_rl/trainer/batch.py292CODE
LOWsrc/prime_rl/trainer/batch.py538CODE
LOWsrc/prime_rl/trainer/model.py140CODE
LOWsrc/prime_rl/trainer/model.py385CODE
LOWsrc/prime_rl/trainer/model.py765CODE
LOWsrc/prime_rl/trainer/model.py905CODE
LOWsrc/prime_rl/trainer/model.py1167CODE
LOWsrc/prime_rl/trainer/model.py1258CODE
LOWsrc/prime_rl/trainer/model.py188CODE
LOWsrc/prime_rl/trainer/lora.py218CODE
LOWsrc/prime_rl/trainer/optim.py164CODE
LOWsrc/prime_rl/trainer/optim.py28CODE
LOWsrc/prime_rl/trainer/models/kernels/sparse_mla_bwd.py98CODE
LOWsrc/prime_rl/trainer/models/kernels/sparse_mla_bwd.py146CODE
LOWsrc/prime_rl/trainer/models/qwen3_5_moe/mrope.py31CODE
LOWsrc/prime_rl/trainer/sft/train.py61CODE
LOWsrc/prime_rl/trainer/sft/data.py119CODE
LOWsrc/prime_rl/trainer/sft/data.py436CODE
LOWsrc/prime_rl/trainer/rl/train.py72CODE
LOWsrc/prime_rl/orchestrator/dispatcher.py240CODE
LOWsrc/prime_rl/orchestrator/train_sink.py172CODE
LOWsrc/prime_rl/orchestrator/inference_metrics.py136CODE
LOWsrc/prime_rl/orchestrator/trajectories.py69CODE
LOWsrc/prime_rl/orchestrator/filters.py149CODE
LOWsrc/prime_rl/orchestrator/algo/echo.py42CODE
Modern AI Meta-Vocabulary10 hits · 30 pts
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MEDIUMREADME.md363. Native integration with [`verifiers`](https://github.com/PrimeIntellect-ai/verifiers) environments through the [EnvirCODE
MEDIUMtests/unit/orchestrator/test_algorithms.py300 # template scaffold (False), the second is message body (True). Only the bodyCOMMENT
MEDIUMtests/unit/orchestrator/test_algorithms.py301 # token gets the role weight — the scaffold is excluded (content granularity).COMMENT
MEDIUMtests/unit/orchestrator/test_algorithms.py324 # Only position 5 (the body token) is weighted; the scaffold token at position 4 is not.COMMENT
MEDIUMdocs/inference.md29- [Disaggregated](#pd-disaggregation) - Runs the inference server on multiple nodes, but disaggregates the prefill and dCODE
MEDIUMdocs/configuration.md250- [**MiniMax-M2.5 SWE**](https://github.com/PrimeIntellect-ai/prime-rl/tree/main/examples/advanced/minimax-m2.5) — `MiniCODE
MEDIUMexamples/advanced/glm-4.5-air/terminal.toml1# exp: GLM-4.5-Air (100B MoE) RL across our v1 TERMINAL-domain envs — agentic shell/sandbox tasksCOMMENT
MEDIUMsrc/prime_rl/trainer/sft/data.py295 # scaffolding (e.g. <|im_start|>assistant) the harness emits.COMMENT
MEDIUMsrc/prime_rl/trainer/sft/data.py400 # If processed example is None, skip it (e.g. if tokenized sample exceeds context window)COMMENT
MEDIUMsrc/prime_rl/orchestrator/filters.py55 # generation-prompt scaffold that token_ids/mask also span).COMMENT
AI Structural Patterns32 hits · 30 pts
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LOW…/train/models/afmoe_hf_modeling/configuration_afmoe.py36CODE
LOW…/unit/train/models/afmoe_hf_modeling/modeling_afmoe.py597CODE
LOWtests/unit/orchestrator/test_metrics.py14CODE
LOWsrc/prime_rl/utils/pathing.py13CODE
LOWsrc/prime_rl/utils/monitor/__init__.py35CODE
LOWsrc/prime_rl/inference/patches.py339CODE
LOW…prime_rl/trainer/models/laguna/configuration_laguna.py37CODE
LOWsrc/prime_rl/trainer/models/laguna/modeling_laguna.py334CODE
LOWsrc/prime_rl/trainer/models/kernels/sparse_mla_bwd.py98CODE
LOWsrc/prime_rl/trainer/models/gpt_oss/modeling_gpt_oss.py292CODE
LOWsrc/prime_rl/trainer/models/layers/moe.py122CODE
LOWsrc/prime_rl/trainer/models/layers/moe.py828CODE
LOWsrc/prime_rl/trainer/models/layers/moe.py782CODE
LOWsrc/prime_rl/trainer/models/layers/moe.py1234CODE
LOW…/trainer/models/minimax_m2/configuration_minimax_m2.py66CODE
LOW…me_rl/trainer/models/minimax_m2/modeling_minimax_m2.py205CODE
LOW…/trainer/models/nemotron_h/configuration_nemotron_h.py67CODE
LOW…me_rl/trainer/models/nemotron_h/modeling_nemotron_h.py480CODE
LOWsrc/prime_rl/trainer/models/qwen3/modeling_qwen3.py221CODE
LOW…_rl/trainer/models/qwen3_5_moe/modeling_qwen3_5_moe.py945CODE
LOW…rainer/models/qwen3_5_moe/configuration_qwen3_5_moe.py18CODE
LOW…_rl/trainer/models/glm_moe_dsa/modeling_glm_moe_dsa.py291CODE
LOW…rainer/models/glm_moe_dsa/configuration_glm_moe_dsa.py127CODE
LOW…c/prime_rl/trainer/models/afmoe/configuration_afmoe.py18CODE
LOWsrc/prime_rl/trainer/models/afmoe/modeling_afmoe.py448CODE
LOW…/prime_rl/trainer/models/glm4_moe/modeling_glm4_moe.py248CODE
LOW…e_rl/trainer/models/glm4_moe/configuration_glm4_moe.py144CODE
LOWsrc/prime_rl/trainer/models/qwen3_5/modeling_qwen3_5.py406CODE
LOWsrc/prime_rl/trainer/models/llama/modeling_llama.py243CODE
LOW…rime_rl/trainer/models/qwen3_moe/modeling_qwen3_moe.py252CODE
LOW…rl/trainer/models/qwen3_moe/configuration_qwen3_moe.py142CODE
LOWsrc/prime_rl/trainer/sft/data.py183CODE
Over-Commented Block20 hits · 20 pts
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LOW…/train/models/afmoe_hf_modeling/configuration_afmoe.py1# coding=utf-8COMMENT
LOWexamples/advanced/glm-4.5-air/terminal.toml141labels = ["glm45air-terminal"]COMMENT
LOWexamples/advanced/glm-4.5-air/terminal.toml181id = "terminal-bench-2-v1"COMMENT
LOWexamples/advanced/glm-4.5-air/swe.toml1# v1 RL: GLM-4.5-Air (100B MoE) on the v1 `scaleswe-v1` SWE taskset, rlm harness on a prime sandboxCOMMENT
LOWscripts/install_llmd.sh1#!/usr/bin/env bashCOMMENT
LOWscripts/install_ep_kernels.sh1#!/usr/bin/env bashCOMMENT
LOWscripts/docker-entrypoint.sh1#!/bin/bashCOMMENT
LOWscripts/docker-entrypoint.sh41 git -C "$DEST" reset --hard --quiet "origin/${PRIME_RL_REF}" 2>/dev/null || trueCOMMENT
LOWscripts/install_deep_gemm.sh1#!/usr/bin/env bashCOMMENT
LOW…ages/prime-rl-configs/src/prime_rl/utils/validation.py141 propagate("output_dir", "trainer.output_dir", "orchestrator.output_dir")COMMENT
LOWsrc/prime_rl/_compat.py21COMMENT
LOWsrc/prime_rl/transport/types.py41 # orchestrator batches per-image renderer items by torch.cat alongCOMMENT
LOWsrc/prime_rl/utils/act_offloading.py261 # tensor.COMMENT
LOWsrc/prime_rl/inference/vllm/serving_tokens.py241 # ``/inference/v1/generate`` — decode receives an empty NIXL handshakeCOMMENT
LOWsrc/prime_rl/trainer/ckpt.py121 return state_dictCOMMENT
LOWsrc/prime_rl/trainer/parallel_dims.py1# Copyright (c) Meta Platforms, Inc. and affiliates.COMMENT
LOWsrc/prime_rl/trainer/models/layers/moe.py741 # tokens_per_expert will be used to update the expert bias for load balancing.COMMENT
LOW…/prime_rl/trainer/models/glm4_moe/modeling_glm4_moe.py1# coding=utf-8COMMENT
LOWsrc/prime_rl/trainer/models/llama/modeling_llama.py1# coding=utf-8COMMENT
LOW…rime_rl/trainer/models/qwen3_moe/modeling_qwen3_moe.py1# coding=utf-8COMMENT
Hallucination Indicators1 hit · 15 pts
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CRITICALsrc/prime_rl/utils/act_offloading.py214 torch.autograd.variable.Variable._execution_engine.queue_callback(wait_and_del_remaining_references)CODE
Redundant / Tautological Comments7 hits · 10 pts
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LOWscripts/chat.py70 # Check if the non-whitespace starts with a thinking tagCOMMENT
LOWscripts/chat.py92 # Check if we might be building up to a thinking tagCOMMENT
LOWscripts/chat.py117 # Check if buffer might contain partial closing tagCOMMENT
LOW.github/workflows/nightly_tests.yaml94 # Set WANDB_MODE to online only if WANDB_API_KEY is available, otherwise set to offlineCOMMENT
LOW.github/workflows/cpu_tests.yaml33 # Set WANDB_MODE to online only if WANDB_API_KEY is available, otherwise set to offlineCOMMENT
LOWsrc/prime_rl/entrypoints/rl.py400 # Check if any critical process failedCOMMENT
LOWsrc/prime_rl/trainer/lora.py167 # Check if module is Linear or one of the supported expert classesCOMMENT
Cross-Language Confusion3 hits · 10 pts
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HIGHtests/unit/train/sft/test_sft_dataset.py51 pytest.param('{"reasoning_effort": null}', id="json-string"),CODE
HIGHpackages/prime-rl-configs/src/prime_rl/utils/config.py12 TOML cannot represent null, so None fields left at their default are droppedSTRING
HIGHsrc/prime_rl/trainer/sft/data.py139 returns ``True`` even when the value is null). Strip the noise beforeSTRING
Structural Annotation Overuse5 hits · 8 pts
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LOWexamples/advanced/glm-5.2/infer/pd-llmd.toml2# Step 3 / goal target. Topology mirrors the proven original glm5.1_llmd_inferenceCOMMENT
LOWexamples/basic/reverse-text/README.md112### Step 1: Deploy for SFT TrainingCOMMENT
LOWexamples/basic/reverse-text/README.md128### Step 2: Run SFT TrainingCOMMENT
LOWexamples/basic/reverse-text/README.md148### Step 3: Deploy RL TrainingCOMMENT
LOWexamples/basic/reverse-text/README.md203### Step 4: Run EvaluationCOMMENT
Self-Referential Comments2 hits · 4 pts
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MEDIUMscripts/chat.py234 # Create the demoCOMMENT
MEDIUMsrc/prime_rl/trainer/models/layers/moe.py400 """This class implements token-choice routing. In token-choice top-K routing, each token isSTRING
Example Usage Blocks2 hits · 3 pts
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LOWscripts/install_ep_kernels.sh14# Usage:COMMENT
LOWscripts/install_deep_gemm.sh12# Usage:COMMENT
Fake / Example Data3 hits · 2 pts
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LOWconfigs/README.md11 (per-algorithm smokes), `fake/` (fake-data trainer/SFT smokes), and `multi-env/`CODE
LOW…kages/prime-rl-configs/src/prime_rl/configs/trainer.py111 """Replace MoE token-choice routing with a round-robin assignment so every expert sees an equal share. Intended for STRING
LOWsrc/prime_rl/trainer/model.py460 """Force MoE token-choice routers into round-robin assignment for fake-data smoke tests."""STRING