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OPD Before RL: Warm-Starting Rubric-Based RL with On-Policy Distillation

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Based on a feed excerpt; full article content has not been acquired. Source evidence retrieved 07/10/2026, 15:18:24 UTC.

Briefings

A two-stage training framework improves rubric-based reinforcement learning by using on-policy distillation to learn token-level supervision before RL optimization. Evaluated on HealthBench, ResearchQA, and RubricHub Science, the method achieves the highest scores among evaluated approaches. The framework avoids reward hacking on RubricHub Science, unlike the SFT baseline which receives high rewards for non-compliant content.

Key points

• The proposed framework uses rubric-privileged on-policy distillation (RP-OPD) to match teacher next-token distributions at student-generated prefixes before RL optimization.
• RP-OPD + RL achieves the highest scores on HealthBench, ResearchQA, and RubricHub Science compared to other evaluated post-training methods.

Context

Rubric-based reinforcement learning scores open-ended responses against explicit criteria but lacks direct identification of contributing decisions. This study proposes a two-stage framework using rubrics as privileged teacher context for dense token-level supervision, then as rewards for further RL optimization.

Limitations

Evaluation is limited to HealthBench, ResearchQA, and RubricHub Science. The framework shows limited signs of reward hacking on RubricHub Science, whereas the SFT baseline increasingly receives high rewards for claims of rubric compliance without providing required content. Uncertainty remains regarding the generalizability of the two-stage approach across diverse health and science tasks.

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