Co-Evolving Policy and Rubric via Capability Awareness for Open-Ended Generation
arXiv:2610.38566v1 Announce Type: new Abstract: Rubric-based reinforcement learning (RL) offers interpretable, fine-grained supervision for aligning large language models (LLMs) on open-ended generation tasks. However, static rubric criteria may become saturated or remain beyond the policy's exploration, rendering the reward signals insufficiently discriminative. While recent efforts seek to guide sampling exploration or evolve rubrics, they fail to explicitly factor in how rubric difficulty sh…