RISE-RL: Rubric-Informed Selective Exploration for Open-Ended Reinforcement Learning

Aligning Large Language Models (LLMs) for open-ended tasks is challenging because responses must satisfy multidimensional criteria without following a single correct generation trajectory. Existing rubric-based reinforcement learning (RL) methods compress fine-grained criterion-level feedback into scalar rewards, making persistent capability gaps difficult to target under limited on-policy exploration. We propose $\textbf{RISE-RL}$ (Rubric-Informed Selective Exploration), which uses repeatedly m

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ethics.ai (10 August 2026), “RISE-RL: Rubric-Informed Selective Exploration for Open-Ended Reinforcement Learning,” evidence record 18028, https://ethics.ai/record/18028 (originally published by arXiv).

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