Repository Analysis

walkinglabs/hands-on-modern-rl

🚀 An open-source, hands-on curriculum bridging the gap from basic RL concepts to LLM alignment, RLVR, and advanced Agentic systems.

16.8 Moderate AI signal View on GitHub

Analysis Overview

This report presents the forensic synthetic code analysis of walkinglabs/hands-on-modern-rl, a Python project with 3,670 GitHub stars. SynthScan v2.0 examined 165,476 lines of code across 494 source files, recording 1067 pattern matches distributed across 14 syntactic categories. The overall adjusted score of 16.8 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).

16.8
Adjusted Score
16.8
Raw Score
100%
Time Factor
2026-08-09
Last Push
3.7K
Stars
Python
Language
165.5K
Lines of Code
494
Files
1.1K
Pattern Hits
2026-08-09
Scan Date
0.02
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 0HIGH 12MEDIUM 790LOW 265

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 1067 distinct pattern matches across 14 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.

Decorative Section Separators712 hits · 2228 pts
SeverityFileLineSnippetContext
MEDIUM…de/appendix_common_pitfalls/debug_training_collapse.py46# ==========================================COMMENT
MEDIUM…de/appendix_common_pitfalls/debug_training_collapse.py48# ==========================================COMMENT
MEDIUM…de/appendix_common_pitfalls/debug_training_collapse.py96# ==========================================COMMENT
MEDIUM…de/appendix_common_pitfalls/debug_training_collapse.py98# ==========================================COMMENT
MEDIUM…de/appendix_common_pitfalls/debug_training_collapse.py207# ==========================================COMMENT
MEDIUM…de/appendix_common_pitfalls/debug_training_collapse.py209# ==========================================COMMENT
MEDIUM…de/appendix_common_pitfalls/debug_training_collapse.py311# ==========================================COMMENT
MEDIUM…de/appendix_common_pitfalls/debug_training_collapse.py313# ==========================================COMMENT
MEDIUM…de/appendix_common_pitfalls/debug_training_collapse.py435# ==========================================COMMENT
MEDIUM…de/appendix_common_pitfalls/debug_training_collapse.py437# ==========================================COMMENT
MEDIUM…de/appendix_common_pitfalls/debug_training_collapse.py537# ==========================================COMMENT
MEDIUM…de/appendix_common_pitfalls/debug_training_collapse.py539# ==========================================COMMENT
MEDIUM…de/appendix_common_pitfalls/debug_training_collapse.py645# ==========================================COMMENT
MEDIUM…de/appendix_common_pitfalls/debug_training_collapse.py647# ==========================================COMMENT
MEDIUM…de/appendix_common_pitfalls/debug_training_collapse.py746# ==========================================COMMENT
MEDIUM…de/appendix_common_pitfalls/debug_training_collapse.py748# ==========================================COMMENT
MEDIUMcode/appendix_common_pitfalls/debug_reward_hacking.py41# ==========================================COMMENT
MEDIUMcode/appendix_common_pitfalls/debug_reward_hacking.py43# ==========================================COMMENT
MEDIUMcode/appendix_common_pitfalls/debug_reward_hacking.py157# ==========================================COMMENT
MEDIUMcode/appendix_common_pitfalls/debug_reward_hacking.py159# ==========================================COMMENT
MEDIUMcode/appendix_common_pitfalls/debug_reward_hacking.py234# ==========================================COMMENT
MEDIUMcode/appendix_common_pitfalls/debug_reward_hacking.py236# ==========================================COMMENT
MEDIUMcode/appendix_common_pitfalls/debug_reward_hacking.py315# ==========================================COMMENT
MEDIUMcode/appendix_common_pitfalls/debug_reward_hacking.py317# ==========================================COMMENT
MEDIUMcode/appendix_common_pitfalls/debug_reward_hacking.py389# ==========================================COMMENT
MEDIUMcode/appendix_common_pitfalls/debug_reward_hacking.py391# ==========================================COMMENT
MEDIUMcode/appendix_common_pitfalls/debug_reward_hacking.py460# ==========================================COMMENT
MEDIUMcode/appendix_common_pitfalls/debug_reward_hacking.py462# ==========================================COMMENT
MEDIUMcode/appendix_common_pitfalls/debug_reward_hacking.py524# ==========================================COMMENT
MEDIUMcode/appendix_common_pitfalls/debug_reward_hacking.py526# ==========================================COMMENT
MEDIUMcode/appendix_common_pitfalls/debug_reward_hacking.py590# ==========================================COMMENT
MEDIUMcode/appendix_common_pitfalls/debug_reward_hacking.py592# ==========================================COMMENT
MEDIUMcode/chapter08_rlhf/sft_pipeline.py23# ==========================================COMMENT
MEDIUMcode/chapter08_rlhf/sft_pipeline.py25# ==========================================COMMENT
MEDIUMcode/chapter08_rlhf/sft_pipeline.py99# ==========================================COMMENT
MEDIUMcode/chapter08_rlhf/sft_pipeline.py101# ==========================================COMMENT
MEDIUMcode/chapter08_rlhf/sft_pipeline.py125# ==========================================COMMENT
MEDIUMcode/chapter08_rlhf/sft_pipeline.py127# ==========================================COMMENT
MEDIUMcode/chapter08_rlhf/rlhf_ppo_train.py38# ==========================================COMMENT
MEDIUMcode/chapter08_rlhf/rlhf_ppo_train.py40# ==========================================COMMENT
MEDIUMcode/chapter08_rlhf/rlhf_ppo_train.py98# ==========================================COMMENT
MEDIUMcode/chapter08_rlhf/rlhf_ppo_train.py100# ==========================================COMMENT
MEDIUMcode/chapter08_rlhf/rlhf_ppo_train.py219# ==========================================COMMENT
MEDIUMcode/chapter08_rlhf/rlhf_ppo_train.py221# ==========================================COMMENT
MEDIUMcode/chapter08_rlhf/rlhf_ppo_train.py419# ==========================================COMMENT
MEDIUMcode/chapter08_rlhf/rlhf_ppo_train.py421# ==========================================COMMENT
MEDIUMcode/chapter08_rlhf/rlhf_ppo_train.py467# ==========================================COMMENT
MEDIUMcode/chapter08_rlhf/rlhf_ppo_train.py469# ==========================================COMMENT
MEDIUMcode/chapter08_rlhf/reward_model_training.py32# ==========================================COMMENT
MEDIUMcode/chapter08_rlhf/reward_model_training.py34# ==========================================COMMENT
MEDIUMcode/chapter08_rlhf/reward_model_training.py153# ==========================================COMMENT
MEDIUMcode/chapter08_rlhf/reward_model_training.py155# ==========================================COMMENT
MEDIUMcode/chapter08_rlhf/reward_model_training.py213# ==========================================COMMENT
MEDIUMcode/chapter08_rlhf/reward_model_training.py215# ==========================================COMMENT
MEDIUMcode/chapter08_rlhf/reward_model_training.py265# ==========================================COMMENT
MEDIUMcode/chapter08_rlhf/reward_model_training.py267# ==========================================COMMENT
MEDIUMcode/chapter08_rlhf/reward_model_training.py390# ==========================================COMMENT
MEDIUMcode/chapter08_rlhf/reward_model_training.py392# ==========================================COMMENT
MEDIUMcode/chapter08_rlhf/reward_model_training.py427# ==========================================COMMENT
MEDIUMcode/chapter08_rlhf/reward_model_training.py429# ==========================================COMMENT
652 more matches not shown…
Structural Annotation Overuse111 hits · 175 pts
SeverityFileLineSnippetContext
LOWdocs/chapter22_agentic/build-agentic-training-system.md498 # Step 1: 把对话历史拼成模型能理解的 promptCOMMENT
LOWdocs/chapter22_agentic/build-agentic-training-system.md500 # Step 2: 模型生成动作(推理,不计算梯度)COMMENT
LOWdocs/chapter22_agentic/build-agentic-training-system.md502 # Step 3: 从自由文本输出中解析结构化动作COMMENT
LOWdocs/chapter22_agentic/build-agentic-training-system.md516 # Step 4: 环境执行动作,返回观测和终止状态COMMENT
LOWdocs/chapter22_agentic/build-agentic-training-system.md519 # Step 5: 记录本轮交互到轨迹COMMENT
LOWdocs/chapter22_agentic/build-agentic-training-system.md527 # Step 6: 把本轮交互加入对话历史,供下一轮使用COMMENT
LOWdocs/chapter22_agentic/build-agentic-training-system.md535 # Step 7: 计算整条轨迹的 reward(只有轨迹结束时才给)COMMENT
LOWdocs/chapter18_grpo/on-policy-distillation.md293# Step 1: student 生成回答COMMENT
LOWdocs/chapter18_grpo/on-policy-distillation.md304# Step 2: 计算 student 和 teacher 的逐 token log-probCOMMENT
LOWdocs/chapter18_grpo/on-policy-distillation.md319# Step 3: 计算逐 token reward(teacher 认可度 - student 自信度)COMMENT
LOWdocs/chapter18_grpo/on-policy-distillation.md342 # Step 1: student rolloutCOMMENT
LOWdocs/chapter18_grpo/on-policy-distillation.md347 # Step 2: 计算 per-token advantageCOMMENT
LOWdocs/chapter18_grpo/on-policy-distillation.md351 # Step 3: 策略梯度更新COMMENT
LOWdocs/chapter29_visual_generation/intro.md742 # Step 3: score final imagesCOMMENT
LOWdocs/chapter29_visual_generation/intro.md746 # Step 4: turn rewards into advantagesCOMMENT
LOWdocs/en/chapter01_cartpole/principles.md360 # Step 1: Collect experience data (2048 steps)COMMENT
LOWdocs/en/chapter01_cartpole/principles.md363 # Step 2: Compute GAE advantagesCOMMENT
LOWdocs/en/chapter01_cartpole/principles.md366 # Step 3: PPO update (train on the same data for 10 epochs)COMMENT
LOWdocs/en/chapter01_cartpole/intro.md41### Step 1: Install DependenciesCOMMENT
LOWdocs/en/chapter01_cartpole/intro.md51### Step 2: Run TrainingCOMMENT
LOWdocs/en/chapter01_cartpole/intro.md80### Step 3: Where to View SwanLab Training CurvesCOMMENT
LOWdocs/en/appendix_code_cheatsheet/grpo-rlvr.md28# Step 1: sample G completions for one prompt, score eachCOMMENT
LOWdocs/en/appendix_code_cheatsheet/grpo-rlvr.md31# Step 2: within-group normalization (subtract mean, divide by std) -> advantageCOMMENT
LOWdocs/en/appendix_code_cheatsheet/grpo-rlvr.md34# Step 3: PPO clipped loss (advantage comes from step 2, not a critic)COMMENT
LOWdocs/en/appendix_code_cheatsheet/grpo-rlvr.md40# Step 4: k3 KL penalty (pull back, don't drift too far from reference)COMMENT
LOWdocs/en/appendix_code_cheatsheet/grpo-rlvr.md44# Step 5: total lossCOMMENT
LOWdocs/en/appendix_code_cheatsheet/grpo-rlvr.md129# Step 1: the RM assigns a scalar score to each answerCOMMENT
LOWdocs/en/appendix_code_cheatsheet/grpo-rlvr.md133# Step 2: we want r_w > r_l; sigmoid the diff and take the negative logCOMMENT
LOWdocs/en/chapter15_rlhf/standard-rlhf-pipeline.md103## Step 0: Choose a Base CheckpointCOMMENT
LOWdocs/en/chapter15_rlhf/standard-rlhf-pipeline.md125## Step 1: SFT Teaches "How To Answer"COMMENT
LOWdocs/en/chapter15_rlhf/standard-rlhf-pipeline.md137## Step 2: The Reward Model Teaches "What Is Better"COMMENT
LOWdocs/en/chapter15_rlhf/standard-rlhf-pipeline.md169## Step 3: PPO-RLHF Optimizes the Policy Under ConstraintsCOMMENT
LOWdocs/en/chapter22_agentic/deep-research-agent.md453### Step 1: Define Multi-Dimensional Rubrics for AI SearchCOMMENT
LOWdocs/en/chapter22_agentic/deep-research-agent.md467### Step 2: From Rubrics to Reward ModelCOMMENT
LOWdocs/en/chapter22_agentic/deep-research-agent.md496### Step 3: Train Search Agent with RLCOMMENT
LOWdocs/en/chapter22_agentic/deep-research-agent.md536### Step 4: Reward Hacking Detection and MitigationCOMMENT
LOWdocs/en/chapter22_agentic/deep-research-agent.md548### Step 5: Search Quality Evaluation and IterationCOMMENT
LOWdocs/en/chapter22_agentic/deep-research-agent.md632#### Step 1: Create Training EnvironmentCOMMENT
LOWdocs/en/chapter22_agentic/agent-data-swe-smith.md45## Step 1: Environment Construction: One Repository, One Docker ImageCOMMENT
LOWdocs/en/chapter22_agentic/agent-data-swe-smith.md59## Step 2: Bug Injection: Four StrategiesCOMMENT
LOWdocs/en/chapter22_agentic/agent-data-swe-smith.md172## Step 3: Test Validation: Use the Test Suite as a SieveCOMMENT
LOWdocs/en/chapter22_agentic/agent-data-swe-smith.md181## Step 4: Generate Issue TextCOMMENT
LOWdocs/en/chapter22_agentic/projects.md316## Step 2: Agent Prompt and Tool-Call ParsingCOMMENT
LOWdocs/en/chapter22_agentic/projects.md368## Step 3: Multi-Tool Agent RolloutCOMMENT
LOWdocs/en/chapter22_agentic/projects.md518## Step 4: Baseline Evaluation: Single-Turn No-Tool vs Single-Tool vs Multi-ToolCOMMENT
LOWdocs/en/chapter22_agentic/projects.md585## Step 5: Batch Rollout and GRPO RL TrainingCOMMENT
LOWdocs/en/chapter22_agentic/projects.md720## Step 6: Post-Training Evaluation: Did the Model Really Learn a Multi-Tool Strategy?COMMENT
LOWdocs/en/chapter22_agentic/projects.md1398### Step 1: Build the Agent EnvironmentCOMMENT
LOWdocs/en/chapter22_agentic/projects.md94## Step 1: Load the Model, Build the Tool Environment, and Load DataCOMMENT
LOWdocs/en/chapter22_agentic/projects.md1269### Step 1: Define Multidimensional Rubrics for AI SearchCOMMENT
LOWdocs/en/chapter22_agentic/projects.md1283### Step 2: From Rubrics to Reward ModelCOMMENT
LOWdocs/en/chapter22_agentic/projects.md1312### Step 3: Train the Search Agent with RLCOMMENT
LOWdocs/en/chapter22_agentic/projects.md1352### Step 4: Detect and Mitigate Reward HackingCOMMENT
LOWdocs/en/chapter22_agentic/projects.md1364### Step 5: Evaluate and Iterate on Search QualityCOMMENT
LOWdocs/en/chapter22_agentic/projects.md1455### Step 2: Define the Tool-Call FormatCOMMENT
LOWdocs/en/chapter22_agentic/projects.md1546### Step 4: Run TrainingCOMMENT
LOWdocs/en/chapter22_agentic/projects.md1485### Step 3: GRPO Training FrameworkSTRING
LOWdocs/en/chapter22_agentic/rllm-deepcoder-lab.md106## Step 1: Designing Rewards with rLLMCOMMENT
LOWdocs/en/chapter22_agentic/rllm-deepcoder-lab.md285## Step 2: What Does the Data Look Like?COMMENT
LOWdocs/en/chapter22_agentic/rllm-deepcoder-lab.md387## Step 3: What Does Model Output Look Like? How Does the Evaluator Score It?STRING
51 more matches not shown…
Modern AI Meta-Vocabulary75 hits · 166 pts
SeverityFileLineSnippetContext
MEDIUMplanning/outline-gap-analysis.md97| #3 | Agentic Rollout 基础设施(Qwen3-Coder 20000 envs,Kimi K2 agentic rollout infra) |CODE
MEDIUMplanning/outline-final-0622.md1131### 22.6 多智能体协作与 Agent Swarm → `chapter10_agentic_rl/multi-agent-swarm.md` `[v5.2 新增]`COMMENT
MEDIUMplanning/outline-final-0622.md1577### 32.3 In-Context RL 与未来十年 → `chapter12_future_trends/llm-multi-agent-rl/index.md`COMMENT
MEDIUMplanning/outline-final-0622.md1613### A.3 Agent / Long-trace 排查清单 → `appendix_common_pitfalls/agentic-failure.md`COMMENT
MEDIUMplanning/outline-final-0622.md1635### B.2 Agent 沙箱与评测工程 → `appendix_industrial_training/agentic-rl-infra.md`COMMENT
MEDIUMdocs/appendix_game_projects/intro.md426| 现代后训练工程 | [NVIDIA NeMo-RL](https://github.com/NVIDIA-NeMo/RL)、[verl](https://github.com/verl-project/verl)、[OpenCODE
MEDIUMdocs/appendix_game_projects/intro.md428| 开源大模型底座 | [Qwen3.6](https://github.com/QwenLM/Qwen3.6)、[Qwen3](https://github.com/QwenLM/Qwen3)、[Meta Llama ModeCODE
MEDIUMdocs/appendix_game_projects/intro.md430| Agent 框架与工具调用 | [OpenAI Agents SDK](https://github.com/openai/openai-agents-python)、[Google ADK](https://github.com/goCODE
MEDIUMdocs/appendix_game_projects/intro.md441- [Agentic RL 与工具调用](#agentic-rl-与工具调用)CODE
MEDIUMdocs/appendix_game_projects/intro.md571| [OpenAI Agents SDK](https://github.com/openai/openai-agents-python) | tools、handCODE
MEDIUMdocs/appendix_game_projects/intro.md575| [AReaL](https://github.com/inclusionAI/AReaL) | 异步 LLM RL CODE
MEDIUMdocs/chapter19_reasoning/adaptive-thinking.md173### 推理与 agentic 的融合COMMENT
MEDIUMdocs/chapter23_rl_based_swe/meta-swe-rl.md148### RAG(Retrieval-Augmented Generation)COMMENT
MEDIUM…cs/chapter23_rl_based_swe/self-play-ssr-and-summary.md114 # 2. Fixer 用 agentic 方式修复STRING
MEDIUMdocs/chapter24_deep_research/intro.md971| **Agentic AI 研究员**(Owkin) | Qwen3-8B 生成创新药物靶点假说,reward 由 5 维 LLM judge 面板给出(新颖性 / 有效性 / 可药性 / 可行性 / 商业价值) CODE
MEDIUMdocs/chapter24_deep_research/intro.md983[^tongyi_dr]: Tongyi DeepResearch Team. "Tongyi DeepResearch Technical Report." [arXiv:2510.24701](https://arxiv.org/absCODE
MEDIUMdocs/chapter22_agentic/industrial-practice.md62> **LinkedIn 团队** 在使用 GPT-OSS(一个 MoE 架构的开源模型)进行 RL 训练时,遇到了梯度爆炸和奖励不增长的问题。经过排查,他们发现根本原因是训练框架中 **Attention Sink 参数的反向传播未被实现CODE
MEDIUMdocs/chapter22_agentic/industrial-practice.md243> **LinkedIn 团队** 在使用 GPT-OSS 进行 RL 训练时发现,MoE 模型的路由网络(Gating Network)在两次前向传播中,可能为同一个 Token 选择不同的专家(Expert),导致 $\log \pi(CODE
MEDIUMdocs/chapter22_agentic/industrial-practice.md249> **Salesforce** 在其 SFR-RL 系统中提出了 **流水线同步 RL(Pipelined Synchronous)** 方案:所有 GPU 在 Rollout 和 Training 两个阶段之间交替切换,而非将 GPU CODE
MEDIUMdocs/chapter22_agentic/industrial-practice.md291- Zhu J, Sang H, et al. "[Unlocking Agentic RL Training for GPT-OSS: A Practical Retrospective](https://huggingface.co/bCODE
MEDIUMdocs/chapter22_agentic/industrial-practice.md295- Salesforce AI Research. "[Building Efficient RL Training for the Agentic Era](https://www.salesforce.com/blog/efficienCODE
MEDIUMdocs/chapter22_agentic/projects.md1602[^tongyi_dr]: Tongyi DeepResearch Team. "Tongyi DeepResearch Technical Report." [arXiv:2510.24701](https://arxiv.org/absCODE
MEDIUMdocs/chapter22_agentic/industrial-evaluation.md58> **LinkedIn 团队** 在使用 GPT-OSS(一个 MoE 架构的开源模型)进行 RL 训练时,遇到了梯度爆炸和奖励不增长的问题。经过排查,他们发现根本原因是训练框架中 **Attention Sink 参数的反向传播未被实现CODE
MEDIUMdocs/chapter22_agentic/industrial-evaluation.md239> **LinkedIn 团队** 在使用 GPT-OSS 进行 RL 训练时发现,MoE 模型的路由网络(Gating Network)在两次前向传播中,可能为同一个 Token 选择不同的专家(Expert),导致 $\log \pi(CODE
MEDIUMdocs/chapter22_agentic/industrial-evaluation.md245> **Salesforce** 在其 SFR-RL 系统中提出了 **流水线同步 RL(Pipelined Synchronous)** 方案:所有 GPU 在 Rollout 和 Training 两个阶段之间交替切换,而非将 GPU CODE
MEDIUMdocs/chapter22_agentic/industrial-evaluation.md287- Zhu J, Sang H, et al. "[Unlocking Agentic RL Training for GPT-OSS: A Practical Retrospective](https://huggingface.co/bCODE
MEDIUMdocs/chapter22_agentic/industrial-evaluation.md291- Salesforce AI Research. "[Building Efficient RL Training for the Agentic Era](https://www.salesforce.com/blog/efficienCODE
MEDIUMdocs/chapter22_agentic/industrial-evaluation.md1192[^benchmark-exploit]: Berkeley RDI. "[Trustworthy Benchmarks for Contamination](https://rdi.berkeley.edu/blog/trustworthSTRING
MEDIUMdocs/chapter22_agentic/industrial-evaluation.md1194[^abc]: Zhu J, et al. "[Establishing Best Practices for Building Rigorous Agentic Benchmarks](https://arxiv.org/abs/2507STRING
MEDIUMdocs/chapter22_agentic/credit-assignment.md330[StepPO](https://arxiv.org/abs/2604.18401)(2026-04)指出:现有 LLM RL 算法继承了 RLHF/RLVR 的 **token-centric paradigm**——token 是建模和CODE
MEDIUMdocs/chapter22_agentic/credit-assignment.md334[AT²PO](https://arxiv.org/abs/2601.04767)(2026-01)进一步把 tree search 引入 turn-level optimization。它针对多轮 agentic RL 的三个核心挑战——CODE
MEDIUMdocs/chapter22_agentic/credit-assignment.md706[^casurvey]: Zhang C. "[From Reasoning to Agentic: Credit Assignment in Reinforcement Learning for Large Language ModelsCODE
MEDIUMdocs/chapter22_agentic/tool-use-agents.md759- Zhu J, Sang H, et al. "[Unlocking Agentic RL Training for GPT-OSS: A Practical Retrospective](https://huggingface.co/bCODE
MEDIUMdocs/chapter22_agentic/tool-use-agents.md763- Salesforce AI Research. "[Building Efficient RL Training for the Agentic Era](https://www.salesforce.com/blog/efficienCODE
MEDIUMdocs/chapter22_agentic/tool-use-and-trajectory.md1146- Zhu J, Sang H, et al. "[Unlocking Agentic RL Training for GPT-OSS: A Practical Retrospective](https://huggingface.co/bCODE
MEDIUMdocs/chapter22_agentic/tool-use-and-trajectory.md1150- Salesforce AI Research. "[Building Efficient RL Training for the Agentic Era](https://www.salesforce.com/blog/efficienCODE
MEDIUMdocs/chapter16_llm_rl_industrial/distributed-sync.md311[SGLang](https://github.com/sgl-project/sglang) 由 LMSYS 团队开发,在 agentic 场景下比 vLLM 更快:CODE
MEDIUM…hapter16_llm_rl_industrial/industrial-post-training.md153#### 4. Kimi K2 与 agentic intelligence 的数据和工具闭环。COMMENT
MEDIUM…hapter16_llm_rl_industrial/industrial-post-training.md197#### 6. Seed Prover 1.5 与 形式化证明环境里的 agentic RL。COMMENT
MEDIUM…hapter16_llm_rl_industrial/industrial-post-training.md225#### 4. DeepSeek-V3.2 与 从答案 verifier 到 agentic verifier。COMMENT
MEDIUM…hapter16_llm_rl_industrial/industrial-post-training.md647#### 1. 企业小模型 与 RAG、工具、安全和低成本推理。COMMENT
MEDIUM…hapter16_llm_rl_industrial/industrial-post-training.md807[^seed_prover]: [Seed Prover 1.5: Advanced Mathematical Reasoning through a Novel Agentic Architecture](https://seed.bytCODE
MEDIUM…hapter16_llm_rl_industrial/industrial-post-training.md809[^seed1_8]: [Official Release of Seed1.8: A Generalized Agentic Model](https://seed.bytedance.com/en/blog/official-releaCODE
MEDIUM…hapter16_llm_rl_industrial/industrial-post-training.md1017[^sfr_rl]: [Salesforce SFR-RL](https://www.salesforce.com/blog/efficient-rl-training-agentic-era/)CODE
MEDIUMdocs/chapter13_imitation_meta_rl/meta-rl.md78### 元 RL 与 few-shot 学习COMMENT
MEDIUM…s/appendix_industrial_training/industrial-exercises.md279[^openai-agentic]: OpenAI, "Researcher, Agentic Post-Training", <https://openai.com/careers/researcher-agentic-post-traiCODE
MEDIUMdocs/en/appendix_game_projects/intro.md423| Modern post-training engineering | [NVIDIA NeMo-RL](https://github.com/NVIDIA-NeMo/RL), [verl](https://github.com/verlCODE
MEDIUMdocs/en/appendix_game_projects/intro.md425| Open-source base models | [Qwen3.6](https://github.com/QwenLM/Qwen3.6), [Qwen3](https://github.com/QwenLM/QweCODE
MEDIUMdocs/en/appendix_game_projects/intro.md427| Agent frameworks & tool calling | [OpenAI Agents SDK](https://github.com/openai/openai-agents-python), [Google ADK](hCODE
MEDIUMdocs/en/chapter22_agentic/deep-research-agent.md953| **Agentic AI Researcher** (Owkin) | Qwen3-8B generates innovative drug target hypotheses, reward from 5-dimensCODE
MEDIUMdocs/en/chapter22_agentic/industrial-practice.md62> When the **LinkedIn team** used GPT-OSS, an open-source model with an MoE architecture, for RL training, they encounteCODE
MEDIUMdocs/en/chapter22_agentic/industrial-practice.md243> When the **LinkedIn team** used GPT-OSS for RL training, they found that the routing network, or gating network, of thCODE
MEDIUMdocs/en/chapter22_agentic/industrial-practice.md249> **Salesforce** proposed a **pipelined synchronous RL** design in its SFR-RL system. All GPUs alternate between the rolCODE
MEDIUMdocs/en/chapter22_agentic/industrial-practice.md291- Zhu J, Sang H, et al. "[Unlocking Agentic RL Training for GPT-OSS: A Practical Retrospective](https://huggingface.co/bCODE
MEDIUMdocs/en/chapter22_agentic/industrial-practice.md295- Salesforce AI Research. "[Building Efficient RL Training for the Agentic Era](https://www.salesforce.com/blog/efficienCODE
MEDIUMdocs/en/chapter22_agentic/projects.md1331 reward -= 0.3 * hallucination_penalty(result) # hallucination penaltyCODE
MEDIUMdocs/en/chapter22_agentic/projects.md1603[^tongyi_dr]: Tongyi DeepResearch Team. "Tongyi DeepResearch Technical Report." [arXiv:2510.24701](https://arxiv.org/absCODE
MEDIUMdocs/en/chapter22_agentic/industrial-evaluation.md238- Zhu J, Sang H, et al. "[Unlocking Agentic RL Training for GPT-OSS: A Practical Retrospective](https://huggingface.co/bCODE
MEDIUMdocs/en/chapter22_agentic/industrial-evaluation.md242- Salesforce AI Research. "[Building Efficient RL Training for the Agentic Era](https://www.salesforce.com/blog/efficienCODE
MEDIUMdocs/en/chapter22_agentic/tool-use-agents.md748- Cheng M, Ouyang J, Yu S, et al. "[Agent-R1: Training Powerful LLM Agents with End-to-End Reinforcement Learning](httpsCODE
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Hyper-Verbose Identifiers72 hits · 75 pts
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LOWcode/appendix_common_pitfalls/debug_reward_hacking.py527def print_reward_design_principles():CODE
LOWcode/chapter08_rlhf/reward_model_training.py36def generate_preference_pairs():CODE
LOWcode/chapter08_rlhf/reward_model_training.py394def visualize_reward_distributions(chosen_scores, rejected_scores, save_path="output/reward_distribution.png"):CODE
LOWcode/chapter03_mdp/bellman_equation_verify.py80def manual_bellman_expectation():CODE
LOWcode/chapter03_mdp/bellman_equation_verify.py412def print_value_iteration_steps(n_steps=10):CODE
LOWcode/chapter10_agentic_rl/multi_turn_rl.py206def compute_discounted_returns(rewards, gamma=0.99):CODE
LOWcode/chapter10_agentic_rl/mini_deep_research_grpo.py83def search_validity_reward_func(completions, **kwargs) -> list[float]:CODE
LOWcode/chapter11_vlm_rl/vlm_grpo_train.py93def generate_correct_response(gt):CODE
LOWcode/chapter11_vlm_rl/vlm_grpo_train.py391def print_before_after_comparison(samples, history, seed=42):CODE
LOWcode/chapter11_vlm_rl/vlm_grpo_train.py525def demonstrate_grpo_normalization():CODE
LOWcode/chapter11_vlm_rl/vlm_grpo_train.py111def generate_short_correct_response(gt):CODE
LOWcode/chapter11_vlm_rl/vlm_grpo_train.py130def generate_partially_correct_response(gt):CODE
LOWcode/chapter11_vlm_rl/vlm_grpo_train.py144def generate_low_quality_response(gt):CODE
LOW…e/chapter05_policy_gradient/reinforce_with_baseline.py211def train_reinforce_with_baseline(num_episodes=500, gamma=0.99, lr=1e-3):CODE
LOWcode/chapter12_future_trends/multi_agent_marl.py746def print_cooperation_statistics(ind_results, shared_results, n_agents):CODE
LOWcode/chapter09_alignment/dpo_math_reward.py36def create_math_preference_data():CODE
LOWcode/chapter04_dqn/dqn_gym_sb3.py49def maybe_make_swanlab_callback(args: argparse.Namespace):CODE
LOWcode/chapter04_dqn/dqn_gym_sb3.py211def ensure_discrete_action_space(env_id: str) -> None:CODE
LOWcode/chapter04_dqn/dqn_atari_sb3.py53def maybe_make_swanlab_callback(args: argparse.Namespace):CODE
LOWcode/chapter09_grpo_rlvr/grpo_mechanism.py88def generate_synthetic_rewards(group_size=8, seed=42):CODE
LOWcode/chapter09_grpo_rlvr/grpo_mechanism.py113def simulate_critic_predictions(rewards, noise_scale=0.08, seed=123):CODE
LOWcode/chapter09_grpo_rlvr/grpo_mechanism.py139def demonstrate_grpo_step_by_step():CODE
LOWcode/chapter09_grpo_rlvr/grpo_mechanism.py235def plot_reward_normalization(rewards, grpo_advantages):CODE
LOWcode/chapter09_grpo_rlvr/grpo_mechanism.py306def plot_advantage_comparison(grpo_advantages, ppo_advantages):CODE
LOWcode/chapter09_grpo_rlvr/grpo_math_reasoning.py127def extract_answer_from_response(response):CODE
LOWdocs/chapter28_vla/embodied-intelligence/index.md174 def apply_domain_randomization(self):CODE
LOWdocs/.vitepress/theme/Layout.vue306function updateSidebarEdgePosition() {CODE
LOWdocs/.vitepress/theme/Layout.vue512function handleMermaidViewerImageLoad(event) {CODE
LOWdocs/.vitepress/theme/Layout.vue532function handleMermaidViewerPointerDown(event) {CODE
LOWdocs/.vitepress/theme/Layout.vue549function handleMermaidViewerPointerMove(event) {CODE
LOWdocs/.vitepress/theme/Layout.vue564function handleMermaidViewerKeydown(event) {CODE
LOWdocs/.vitepress/theme/Layout.vue654function updateLineHeightFromRange(event) {CODE
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LOWdocs/.vitepress/theme/Layout.vue726function scrollOutlineToActiveItem(activeLink) {CODE
LOWdocs/.vitepress/theme/Layout.vue747function scrollSidebarToActiveItem(activeItem) {CODE
LOWdocs/chapter23_rl_based_swe/swe-bench-and-rlvr.md36 def test_empty_in_lookup_short_circuits(self):CODE
LOWdocs/chapter24_deep_research/intro.md476def train_search_reward_model(preference_data, base_model):CODE
LOWdocs/chapter32_selfplay/llm-multi-agent-rl/index.md287 def compute_multi_agent_reward(self, trajectories):CODE
LOWdocs/appendix_code_cheatsheet/attention-mha.md41def scaled_dot_product_attention(Q, K, V, mask=None):CODE
LOWdocs/appendix_code_cheatsheet/attention-mha.md66def scaled_dot_product_attention(Q, K, V, mask=None):CODE
LOWdocs/chapter15_rlhf/imitation-learning-pipeline.md363def construct_preference_pairs(prompt, responses, judge_outputs):STRING
LOWdocs/chapter22_agentic/industrial-practice.md186def hallucination_aware_reward(answer, tool_results, citations):CODE
LOWdocs/chapter22_agentic/projects.md1295def train_search_reward_model(preference_data, base_model):CODE
LOWdocs/chapter22_agentic/industrial-evaluation.md182def hallucination_aware_reward(answer, tool_results, citations):CODE
LOWdocs/chapter22_agentic/tool-use-agents.md545async def rollout_single_trajectory(model, task, sandbox, max_turns=10):CODE
LOWdocs/chapter22_agentic/tool-use-agents.md613def comprehensive_agent_reward(trajectory, final_result, task):CODE
LOWdocs/chapter22_agentic/build-agentic-training-system.md395 def train_step_with_advantage(self, trajectories: list):CODE
LOWdocs/chapter22_agentic/trajectory-synthesis.md282def trajectory_synthesis_pipeline(CODE
LOWdocs/chapter22_agentic/tool-use-and-trajectory.md278def trajectory_synthesis_pipeline(CODE
LOWdocs/chapter22_agentic/tool-use-and-trajectory.md932async def rollout_single_trajectory(model, task, sandbox, max_turns=10):CODE
LOWdocs/chapter22_agentic/tool-use-and-trajectory.md1000def comprehensive_agent_reward(trajectory, final_result, task):CODE
LOWdocs/chapter22_agentic/code/policy.py41 def train_step_with_advantage(self, trajectories: list):CODE
LOWdocs/chapter26_vlm/vlm-challenges.md46def setup_optimizer_with_lr_decay(model, text_lr=1e-6, vision_lr=1e-7):CODE
LOWdocs/chapter13_imitation_meta_rl/meta-rl.md116def algorithm_distillation_data_generate(env, rl_algorithm, n_runs=1000, n_episodes_per_run=200):CODE
LOWdocs/en/chapter32_selfplay/llm-multi-agent-rl/index.md291 def compute_multi_agent_reward(self, trajectories):CODE
LOW…s/en/chapter32_selfplay/embodied-intelligence/index.md180 def apply_domain_randomization(self):CODE
LOWdocs/en/appendix_code_cheatsheet/attention-mha.md45def scaled_dot_product_attention(Q, K, V, mask=None):CODE
LOWdocs/en/appendix_code_cheatsheet/attention-mha.md70def scaled_dot_product_attention(Q, K, V, mask=None):CODE
LOWdocs/en/chapter15_rlhf/imitation-learning-pipeline.md366def construct_preference_pairs(prompt, responses, judge_outputs):STRING
LOWdocs/en/chapter22_agentic/deep-research-agent.md480def train_search_reward_model(preference_data, base_model):CODE
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LOWcode/chapter01_cartpole/2-pytorch_ppo.py443 except Exception:CODE
LOW…chapter09_continuous_control/ppo_td3_sac_comparison.py96except Exception as e:CODE
LOWcode/chapter10_agentic_rl/generate_synthetic_data.py74 except Exception as e:STRING
LOWcode/chapter18_grpo/verl_code_rlvr/code_reward.py99 except Exception as exc: # noqa: BLE001CODE
LOWcode/chapter04_dqn/dqn_gym_sb3.py37 except Exception:CODE
LOWcode/chapter04_dqn/dqn_gym_sb3.py56 except Exception as exc:CODE
LOWcode/chapter04_dqn/dqn_gym_sb3.py104 except Exception:CODE
MEDIUMcode/chapter04_dqn/dqn_gym_sb3.py100def _on_training_start(self) -> None:CODE
LOWcode/chapter04_dqn/dqn_atari_sb3.py41 except Exception:CODE
LOWcode/chapter04_dqn/dqn_atari_sb3.py60 except Exception as exc:CODE
LOWcode/chapter04_dqn/dqn_atari_sb3.py108 except Exception:CODE
MEDIUMcode/chapter04_dqn/dqn_atari_sb3.py104def _on_training_start(self) -> None:CODE
LOWdocs/chapter22_agentic/tool-use-agents.md174 except Exception:CODE
LOWdocs/chapter22_agentic/tool-use-agents.md460 except Exception as e:CODE
LOWdocs/chapter22_agentic/rllm-deepcoder-lab.md1070 except Exception:CODE
LOWdocs/chapter22_agentic/build-agentic-training-system.md288 except Exception as e:CODE
LOWdocs/chapter22_agentic/tool-use-and-trajectory.md555 except Exception:CODE
LOWdocs/chapter22_agentic/tool-use-and-trajectory.md847 except Exception as e:CODE
LOWdocs/chapter22_agentic/code/environment.py41 except Exception as e:CODE
LOWdocs/chapter26_vlm/easyr1-geoqa.md201 except Exception:CODE
LOWdocs/chapter18_grpo/financial-tool-calling-grpo.md248 except Exception:CODE
LOWdocs/chapter18_grpo/financial-tool-calling-grpo.md345 except Exception:CODE
LOWdocs/en/chapter22_agentic/tool-use-agents.md174 except Exception:CODE
LOWdocs/en/chapter22_agentic/tool-use-agents.md460 except Exception as e:CODE
LOWdocs/en/chapter22_agentic/rllm-deepcoder-lab.md1071 except Exception:CODE
LOW…/en/chapter22_agentic/build-agentic-training-system.md292 except Exception as e:CODE
LOWdocs/en/chapter22_agentic/tool-use-and-trajectory.md558 except Exception:CODE
LOWdocs/en/chapter22_agentic/tool-use-and-trajectory.md850 except Exception as e:CODE
LOWdocs/en/chapter26_vlm/easyr1-geoqa.md204 except Exception:CODE
LOWdocs/en/chapter18_grpo/financial-tool-calling-grpo.md245 except Exception:CODE
LOWdocs/en/chapter18_grpo/financial-tool-calling-grpo.md349 except Exception:CODE
Cross-Language Confusion6 hits · 25 pts
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HIGH…de/appendix_common_pitfalls/debug_training_collapse.py252 agent.buffer.push(state, action, reward, next_state, float(done))CODE
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HIGHcode/chapter08_rlhf/rlhf_ppo_train.py239 总损失 = -L_CLIP + β * KL(π_θ || π_ref)STRING
HIGHcode/chapter08_rlhf/rlhf_ppo_train.py275 KL(π_θ || π_ref) = Σ π_θ * log(π_θ / π_ref)STRING
HIGHcode/chapter04_dqn/double_dqn_cartpole.py252 agent.buffer.push(state, action, reward, next_state, float(done))CODE
HIGHcode/chapter04_dqn/dqn_cartpole.py250 agent.buffer.push(state, action, reward, next_state, float(done))CODE
Deep Nesting21 hits · 18 pts
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LOWcode/chapter03_mdp/gridworld_q_learning.py283CODE
LOWcode/chapter03_mdp/gridworld_q_learning.py55CODE
LOWcode/chapter03_mdp/bellman_equation_verify.py146CODE
LOWcode/chapter03_mdp/bellman_equation_verify.py192CODE
LOWcode/chapter03_mdp/bellman_equation_verify.py412CODE
LOWcode/chapter01_cartpole/2-pytorch_ppo.py275CODE
LOWcode/chapter01_cartpole/plot_curves.py28CODE
LOWcode/chapter11_vlm_rl/geometry_counting_dataset.py125CODE
LOWcode/chapter11_vlm_rl/vlm_grpo_train.py262CODE
LOWcode/chapter11_vlm_rl/vlm_grpo_train.py525CODE
LOWcode/chapter12_future_trends/multi_agent_marl.py123CODE
LOWcode/chapter12_future_trends/tree_of_thought.py110CODE
LOWcode/chapter12_future_trends/tree_of_thought.py154CODE
LOWcode/chapter12_future_trends/tree_of_thought.py194CODE
LOWcode/chapter12_future_trends/tree_of_thought.py376CODE
LOWcode/chapter12_future_trends/tree_of_thought.py57CODE
LOWcode/chapter18_grpo/verl_code_rlvr/code_reward.py53CODE
LOWcode/chapter09_grpo_rlvr/grpo_mechanism.py139CODE
LOWscripts/fix-intro-h1.py50CODE
Unused Imports18 hits · 17 pts
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LOWcode/chapter11_vlm_rl/vlm_grpo_train.py27CODE
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LOWcode/chapter12_future_trends/tree_of_thought.py24CODE
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LOW…s/chapter22_agentic/code/deep_research_rl_benchmark.py13CODE
Cross-File Repetition3 hits · 15 pts
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HIGH…de/appendix_common_pitfalls/debug_training_collapse.py0q 网络:将状态映射到每个动作的 q 值 结构:state_dim → 128 → 128 → action_dimSTRING
HIGHcode/appendix_common_pitfalls/debug_reward_hacking.py0q 网络:将状态映射到每个动作的 q 值 结构:state_dim → 128 → 128 → action_dimSTRING
HIGHcode/chapter04_dqn/double_dqn_cartpole.py0q 网络:将状态映射到每个动作的 q 值 结构:state_dim → 128 → 128 → action_dimSTRING
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LOWcode/chapter03_mdp/bellman_equation_verify.py21COMMENT
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LOWcode/chapter09_continuous_control/sac_halfcheetah.py141#COMMENT
LOWcode/chapter18_grpo/verl_code_rlvr/prepare_data.py1# prepare_data.pyCOMMENT
LOW…8_grpo/verl_code_rlvr/run_qwen_coder_ppo_single_gpu.sh1#!/bin/bashCOMMENT
LOWcode/chapter18_grpo/verl_code_rlvr/code_reward.py1# code_reward.pyCOMMENT
LOWdocs/chapter24_deep_research/intro.md681### 数据准备COMMENT
LOWdocs/chapter24_deep_research/intro.md881# 只有模型生成的 token 参与 loss 计算COMMENT
LOWdocs/chapter32_selfplay/rl-scaling-outlook.md121COMMENT
LOWdocs/en/chapter32_selfplay/rl-scaling-outlook.md121# ==========================================COMMENT
LOWdocs/en/chapter22_agentic/deep-research-agent.md681# Should output OK, no errorsCOMMENT
LOWdocs/en/chapter22_agentic/deep-research-agent.md861COMMENT
LOWdocs/chapter10_ppo/gae-reward-model.md301```pythonCOMMENT
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HIGHdocs/en/chapter22_agentic/deep-research-agent.md253**Problem**: Synthetic data often faces the "not hard enough" problem — research trajectories generated by GPT-4 level mCODE
HIGHdocs/en/chapter22_agentic/projects.md1069**Problem**: Synthetic data often suffers from insufficient difficulty. Research trajectories generated by GPT-4-class mCODE
Magic Placeholder Names1 hit · 5 pts
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AI Slop Vocabulary1 hit · 2 pts
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AI Structural Patterns1 hit · 1 pts
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