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Sebastian Raschka 發布從零開始做推理系列第二輪,主題是具可驗證獎勵的強化學習與 GRPO 實作細節

本期第 5/28 則

中文摘要

內容涵蓋裁剪策略比率、KL 損失項、格式獎勵,以及用 MATH-500 評估檢查點與追蹤熵與優勢值等章節。從貼文只能確認教學大綱,無法判斷程式碼品質與實驗結果。

一龍馬判讀

對想動手訓練推理模型的工程師來說,可當作 GRPO 除錯清單對照;實際採用前仍要看完整影片與程式碼,並留意獎勵駭入等訓練不穩定風險。

原文節錄

Sebastian Raschka · @rasbt

Covering clipped policy ratios, KL loss term, format rewards, and other GRPO tips & tricks.…

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Reasoning From Scratch: Reinforcement Learning with Verifiable Rewards (RLVR) round 2. Covering clipped policy ratios, KL loss term, format rewards, and other GRPO tips & tricks. 00:00 Introduction and recap 01:52 Interpreting basic GRPO training metrics 06:34 Planned improvements to GRPO 08:58 Running longer training jobs with Python scripts 13:39 Running the baseline GRPO training script 17:29 Loading and plotting training logs 19:29 Diagnosing unstable training 23:55 Evaluating checkpoints on MATH-500 26:26 Downloading existing checkpoints 30:09 Tracking advantage statistics 34:53 Understanding entropy 40:32 Computing entropy in PyTorch 44:17 Interpreting entropy values 48:58 Adding entropy tracking to GRPO 53:36 Analyzing advantage and entropy metrics 56:18 Stabilizing GRPO with clipped policy ratios 1:03:27 Implementing the clipped policy loss 1:09:39 Analyzing clipped policy training results 1:11:25 KL divergence and reward hacking 1:15:12 Adding a KL loss term 1:20:34 Limitations of the simplified KL loss 1:23:04 Format rewards and think tags 1:25:47 Adding special tokens to the tokenizer 1:30:29 Implementing the format reward 1:35:56 Analyzing format reward training 1:38:25 Rewarding format only for correct answers 1:40:48 Further GRPO improvements from research 1:45:43 Next steps and distillation

收錄日期
2026-10-11
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2026/10/11 08:38(台北)