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

• URL: http://arxiv.org/abs/2608.09123v1
• Date: Mon, 10 Aug 2026 05:02:28 GMT
• Title: RISE-RL: Rubric-Informed Selective Exploration for Open-Ended Reinforcement Learning
• Abstract summary: We propose $textbfRISE-RL$ (Rubric-Informed Selective Exploration), which uses repeatedly missed rubric criteria to elicit privileged trajectories.<n>RISE-RL retains only trajectories whose complete-rubric reward exceeds the mean reward of natural rollouts.<n>It improves the average score by 1.3 points at the 4B scale and $textbf3.3 points at the 14B scale$, including a $textbf6.0-point$ gain on CreativeWriting-V3.
• Score: 31.35146691876858
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 missed rubric criteria to elicit privileged trajectories that are difficult to discover through unguided exploration alone. RISE-RL retains only trajectories whose complete-rubric reward exceeds the mean reward of natural rollouts, and then re-evaluates them under the original prompt to emphasize behaviors that remain weakly supported by the natural policy. The resulting guidance signal is optimized through a separate auxiliary objective and removed once its additional benefit diminishes. Experiments with 4B and 14B models across writing, chat, health, and science show that RISE-RL achieves the highest mean score on every evaluated benchmark under guidance-free evaluation. Compared with standard Rubric-RL, it improves the average score by 1.3 points at the 4B scale and $\textbf{3.3 points at the 14B scale}$, including a $\textbf{6.0-point}$ gain on CreativeWriting-V3. It also improves creative-writing diversity and yields gains on objectively scored medical and scientific benchmarks. These results indicate that selective internalization through reward filtering and policy support shaping is effective for open-ended reinforcement learning.

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