Best Practice Critic Optimization
Group-based reinforcement learning methods such as GRPO for large language models avoid training a critic by sampling multiple responses for each prompt. A reliable critic could instead estimate token-level advantages from one response, but standard critic-based training recipes are often unstable. We study this instability and develop **Best Practice Critic Optimization (BPCO)**, a recipe that combines DPPO, value predictions bounded to the reward range, Monte Carlo value targets, unnormalized policy advantages, and length-adaptive generalized advantage estimation. Because the critic is used only during training, BPCO can also condition it on reward-defining information, such as a refere...
| Authors | Penghui Qi, Xiangxin Zhou, Wee Sun Lee |
|---|---|
| Primary category | cs.LG |
| Published | 24 August 2026 |
| Last updated at source | 25 August 2026 |
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