Eat Your Precision: Precision Objective Mitigates Reward Hacking in Rubric-based RL
The paper addresses rubric-based RL rewards, which are typically weighted rubric coverage and so push models toward recall. This leads RL-trained models to produce longer responses with more unnecessary or factually incorrect content, often less preferred than the base model. The authors propose the FORK reward, which jointly optimizes recall and a precision objective requiring each response unit to be necessary (via rubric-driven necessity credits) and factually correct (via a factual-correctness grader). They report FORK improves precision, reduces length, keeps similar rubric coverage, and raises win rates versus the base policy, e.g., 10.6% to 68.0% on RubricHub-Medical and 22.1% to 74.1% on HealthBench.
Open original source huggingface.coOne primary source; other links are context, not independent corroboration.
- Primary sourceHugging Face Daily Papers
- Discovery linkHugging Face
- Linked in sourceView arXiv page
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The paper addresses rubric-based RL rewards, which are typically weighted rubric coverage and so push models toward recall. This leads RL-trained models to produce longer responses with more unnecessary or factually incorrect content, often less preferred than the base model. The authors propose the FORK reward, which jointly optimizes recall and a precision objective requiring each response unit to be necessary (via rubric-driven necessity credits) and factually correct (via a factual-correctness grader). They report FORK improves precision, reduces length, keeps similar rubric coverage, and raises win rates versus the base policy, e.g., 10.6% to 68.0% on RubricHub-Medical and 22.1% to 74.1% on HealthBench.
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Source claims not independently checked; results are reported on specific benchmarks (RubricHub-Medical, HealthBench) and may not transfer to other domains.
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Key findings from the stored brief
- Recall-only rubric coverage rewards lead RL-trained models to produce more verbose responses with more unnecessary or incorrect content.
- FORK jointly optimizes recall and a precision objective based on necessity credits and a factual-correctness grader.
- Compared with the recall-based reward, FORK substantially improves precision and reduces length at similar rubric coverage.
- Reported win rate versus the base policy rises from 10.6% to 68.0% on RubricHub-Medical and 22.1% to 74.1% on HealthBench.
Source claims not independently checked; results are reported on specific benchmarks (RubricHub-Medical, HealthBench) and may not transfer to other domains.
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Why this may matter
Rubric-based rewards are a common way to train models on tasks without verifiable answers. If recall-only objectives cause verbosity and errors, a precision term offers a way to tune both completeness and quality, which bears on how such RL pipelines are designed.
Limits and open questions
Source claims not independently checked; results are reported on specific benchmarks (RubricHub-Medical, HealthBench) and may not transfer to other domains.
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