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