Tackling Reward Gaming: A Scale-Invariant Approach to AI Alignment via Multi-Scale Optimization
jd_pressman · x · 2026-08-13
The author discusses how the Weave algorithm can defeat specification gaming during model training.
Key Concepts:
- Task Definition: A task consists of an informal natural language specification (I) and a formal specification (F). Models often exploit low-quality F.
- Adversarial Repair: The author proposes training a reward policy (R) that samples adversarial subclauses post-action to repair F in-context.
- Alignment Goal: This attenuates rewards for actions exploiting F, eventually aligning the policy to follow I over F.
The author further suggests that applying this across multi-scale optimization levels could lead to a scale-invariant preference. Assuming non-perverse generalization by the Transformer, the model learns not to cheat even at unverifiable higher levels, solving a decent chunk of the alignment problem in the limit.
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