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ARXIV:2605.12474 · RL EVALUATION · SUBMITTED 13 MAY · 21:04 UTC · FRESHNESS STALE
ARXIV:2605.12474RL EVALUATIONSUBMITTED 13 MAY · 21:04 UTCFRESHNESS STALEAnas Mahmoud · MohammadHossein Rezaei · Zihao Wang · Anisha Gunjal · Bing Liu · Yunzhong He · arXiv
Investigating reward hacking in rubric-based reinforcement learning to understand and mitigate divergence between training verifiers and human judges.
Opportunity summary
Pain Investigating reward hacking in rubric-based reinforcement learning to understand and mitigate divergence between training verifiers and human judges.
Evidence 0 refs | 3 sources | 50% coverage
Blocker Evidence unverified
Investigating reward hacking in rubric-based reinforcement learning to understand and mitigate divergence between training verifiers and human judges. We study reward hacking in rubric-based RL, where a policy is optimized against a training verifier…
Reinforcement learning with verifiable rewards has enabled strong post-training gains in domains such as math and coding, though many open-ended settings rely on rubric-based rewards. We study reward hacking in rubric-based RL, where a…
ScienceToStartup currently rates this 3.0/10 on the public viability pass. Together, these results suggest that stronger verification reduces reward hacking, but does not by itself ensure that rubric gains correspond to broader quality gains.
RL Evaluation moved forward this cycle; last verified May 2026. Public score 3.0/10.
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Investigating reward hacking in rubric-based reinforcement learning to understand and mitigate divergence between training verifiers and human judges.
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10.48550/arXiv.2605.12474Investigating reward hacking in rubric-based reinforcement learning to understand and mitigate divergence between training verifiers and human judges.
Abstract
Reinforcement learning with verifiable rewards has enabled strong post-training gains in domains such as math and coding, though many open-ended settings rely on rubric-based rewards. We study reward hacking in rubric-based RL, where a policy is optimized against a training verifier but evaluated against a cross-family panel of three frontier judges, reducing dependence on any single evaluator. Our framework separates two sources of divergence: verifier failure, where the training verifier credits rubric criteria that reference verifiers reject, and rubric-design limitations, where even strong rubric-based verifiers favor responses that rubric-free judges rate worse overall. Across medical and science domains, weak verifiers produce large proxy-reward gains that do not transfer to the reference verifiers; exploitation grows over training and concentrates in recurring failures such as partial satisfaction of compound criteria, treating implicit content as explicit, and imprecise topical matching. Stronger verifiers substantially reduce, but do not eliminate, verifier exploitation. We also introduce a self-internalization gap, a verifier-free diagnostic based on policy log-probabilities, which tracks reference-verifier quality, detecting when the policy trained using the weak verifier stops improving. Finally, in our setting, stronger verification does not prevent reward hacking when the rubric leaves important failure modes unspecified: rubric-based verifiers prefer the RL checkpoint, while rubric-free judges prefer the base model. These disagreements coincide with gains concentrated in completeness and presence-based criteria, alongside declines in factual correctness, conciseness, relevance, and overall quality. Together, these results suggest that stronger verification reduces reward hacking, but does not by itself ensure that rubric gains correspond to broader quality gains.
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unverified0 refs; 3 sources; 50% coverage.
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Dimensions overall score 3.0
PROBLEM
Investigating reward hacking in rubric-based reinforcement learning to understand and mitigate divergence between training verifiers and human judges. We study reward hacking in rubric-based RL, where a policy is optimized against a training verifier but evaluated against a cros...
METHOD
Reinforcement learning with verifiable rewards has enabled strong post-training gains in domains such as math and coding, though many open-ended settings rely on rubric-based rewards. We study reward hacking in rubric-based RL, where a policy is optimized against a training veri...
RESULT
ScienceToStartup currently rates this 3.0/10 on the public viability pass. Together, these results suggest that stronger verification reduces reward hacking, but does not by itself ensure that rubric gains correspond to broader quality gains.
WHY NOW
RL Evaluation moved forward this cycle; last verified May 2026. Public score 3.0/10.
Abstract-backed public claims while anchored extraction refreshes.
Investigating reward hacking in rubric-based reinforcement learning to understand and mitigate divergence between training verifiers and human judges. We study reward hacking in rubric-based RL, where a policy is optimized against a training verifier but evaluated against a cross-family panel of three frontier judges, reducing dependence on any single evaluator.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Reinforcement learning with verifiable rewards has enabled strong post-training gains in domains such as math and coding, though many open-ended settings rely on rubric-based rewards. We study reward hacking in rubric-based RL, where a policy is optimized against a training verifier but evaluated against a cross-family panel of three frontier judges, reducing dependence on any single evaluator.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 3.0/10 on the public viability pass. Together, these results suggest that stronger verification reduces reward hacking, but does not by itself ensure that rubric gains correspond to broader quality gains.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
RL Evaluation moved forward this cycle; last verified May 2026. Public score 3.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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Investigating reward hacking in rubric-based reinforcement learning to understand and mitigate divergence between training verifiers and human judges.
Segment
RL Evaluation
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Commercial read
3.0/10 public viability
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missing
reason
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proof status
unverified
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confidence low
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passport absent
stale
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Artifact maturity
GitHub and Hugging Face maturity payloads
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stale
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Technical feasibility
partial
Current read
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Gaps
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Integration burden
missing
Current read
No public implementation surface observed.
Evidence
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Write integration checklist from prototype path and target workflow.
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Classify regulatory flags before commercialization planning.
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Paper authors are not treated as operators without consent.
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Regulatory need unclassified.
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ARTIFACTS
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DEFENSIBILITY
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