CriPO: Enhancing Rubric-based RL via Self-Distillation
Rubric-based Reinforcement Learning (RL) has recently shown promise in improving Large Language Models (LLMs) on open-ended tasks. A widely recognized limitation of rubric-based RL is limited exploration: criteria that no rollout manages to satisfy (Unexplored Criteria) receive no optimization signal. Recent methods address this by incorporating rubric information as external guidance during rollout generation, yet they introduce a train-inference mismatch: the policy is optimized on rollouts produced under external guidance while this guidance is absent at inference time, causing error accumulation through autoregressive decoding. Moreover, these exploration-focused approaches overlook a...
| Authors | Mingxuan Xia, Yuhang Yang, Chao Ye et al. |
|---|---|
| Primary category | cs.LG |
| Published | 20 July 2026 |
| Last updated at source | 27 September 2026 |
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