AI BriefingWeight-Space Extrapolation Reduces Reward Hacking Failures to 8.0% in Code RL
·2026.11.11 00:12
Key point
A steering vector derived from the difference between loose and strict reward checkpoints allows extrapolation to reduce resource-limit failures from 14.5% to 8.0%.
Details
- A single vector in model weight space, calculated as the difference between loose-reward and strict-reward checkpoints, controls reward hacking behavior in LLMs.
- Extrapolating along this vector suppresses resource-limit failures to 8.0%, improving upon the 14.5% achieved by strict-reward RL training alone.
- Stricter rewards impose a "correctness tax," increasing correctness failures on hard problems while reducing resource-limit failures, leaving the overall solve rate nearly unchanged.
- The method, accepted at NeurIPS, involves amplifying unwanted behavior with loose rewards, identifying the weight-space direction, and extrapolating past the strict model to suppress it without additional training.
- Excessive extrapolation leads to output collapse, including malformed outputs and infinite reasoning loops.
- On harder-to-run problems, 71% of commonly solved programs run faster after extrapolation.
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