Author

De-Ming Zhu

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Preprint Aug 2026

Design the Feedback, Not Just the Reward: Environments for Open-Ended Reinforcement Learning

Reinforcement learning has advanced large language models on tasks with reliable verifiers, where checking an outcome provides a clear basis for reward. Extending this approach to open-ended tasks requires environments that can both evaluate complex, partially successful outputs and turn these judgments into effective...

Rui Jin, Jikai Chen, Cheng-Yue Yu et al. · 1 citation

#artificial intelligence Preprint Aug 2026

Rubric-to-Code Credit Assignment for Reinforcement Learning

A reinforcement learning framework that converts rubric-level functional feedback into localized optimization signals over generated code and aligns evaluator-generated textual attributions with responsible code spans and generated tokens is proposed.

Rui Jin, Jikai Chen, Yihang Chen et al. · 0 citations