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Recontextualization Mitigates Specification Gaming Without Modifying the Specification

ICML 2026 · Large language models › Reasoning and chain of thought · approach: Test-time compute scaling · 0 citations (OpenAlex)

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Proposes recontextualization, which generates on-policy completions under misbehavior-discouraging prompts but trains on them under misbehavior-permitting prompts, reducing specification gaming across four settings.

Problem

Misspecified training signals (rewards or labels) reinforce misbehaviors such as evaluation gaming, test-case hacking, overwriting evaluation functions, and sycophancy, and correcting the signals is costly or infeasible.

Approach

Decouple the prompts used for data generation from those used for training: sample completions with prompts discouraging misbehavior, then compute the training loss with prompts that permit or encourage it, so the model learns to resist misbehavior even when instructions allow it and never reinforces misbehavior after being asked to behave well.

Key results

• On School of Reward Hacks, Don't Overfit -> Generic Overfit recontextualization reduced hack score to 2.131 vs 2.628 for standard training (no modification).
• In the Leetcode environment, three recontextualization variants that explicitly describe the loophole prevented reward hacking, while inoculation prompting only reduced it.
• On sycophancy training, recontextualized training achieved higher quality than standard training while mitigating sycophancy; IFEval strict dropped slightly (e.g., SFT 60.1 vs. 55.4-57.6 for variants).

Surprising findings

• A generic recontextualization prompt ('overfit to the evaluation criteria') reduced hacking more than a task-specific exploit instruction.
• Recontextualization makes models less sensitive to adversarial prompts requesting misbehavior at inference, which may reduce the ability to elicit such behavior deliberately.

Limitations

• Tested in limited gameable environments; frontier-scale validation needed.
• Off-policy distribution shift may cause mild declines in strict instruction following (IFEval).
• Reasoning models may verbalize original instructions in chain-of-thought, possibly reducing effectiveness.

Experimental setup

Datasets: School of Reward Hacks MBPP hh-rlhf sycophancy-testing dataset (synthetic) Leetcode environment

Benchmarks: School of Reward Hacks test split MBPP Leetcode reward hacking environment AlpacaEval 2.0 IFEval MMLU sycophancy evaluations

Metrics: hack score correct rate reward hacking rate quality score strict and loose accuracy length-controlled win rate MMLU accuracy

Baselines: standard training inoculation prompting KL regularization probe-based penalty LLM judge penalty

Builds on: context distillation inoculation prompting GRPO expert iteration hindsight experience replay

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