Signed Compression Progress on a Sealed Audit is Goodhart-Resistant

2026-06-09 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Expert, quick

Summary

A new paper introduces "signed compression progress" as a Goodhart-resistant intrinsic motivation mechanism for AI agents. This method rewards an agent based on the signed decrease of a fixed, "sealed-audit" loss function, defined as r_t = E(theta_{t-1}) - E(theta_t). The authors prove that cumulative reward from this approach telescopes precisely to endpoint audit improvement, preventing policies from inflating rewards without genuine performance gains. For finite audit panels, the guarantee holds with a false-positive budget, where cumulative empirical reward is capped by true audit improvement plus 2 Delta_n(F, delta), the uniform audit deviation. The study identifies failure modes, including progress clipping or using reusable panels with high-capacity models. Experiments on ARC-TGI grid-transformation generators confirm the theory, showing finite-audit deviation scales as n^{-0.527} and that signed progress resists common exploitation tactics like clip-farming.

Key takeaway

For AI Scientists designing intrinsic motivation systems, you should consider signed compression progress on a sealed audit to ensure Goodhart resistance. This approach prevents agents from exploiting reward functions without genuine learning, especially when using fixed, non-reusable audit panels. Be cautious: clipping rewards or employing high-capacity models on reusable panels can undermine this guarantee, requiring robust release defenses.

Key insights

The paper proves signed compression progress on a sealed audit provides Goodhart-resistant intrinsic motivation for AI agents.

Principles

• Cumulative signed progress equals endpoint audit improvement.
• Finite audit panels introduce a 2 Delta_n(F, delta) deviation budget.
• Goodhart resistance fails with clipped progress or reusable panels.

Method

Reward an agent with the signed decrease of a fixed, sealed-audit loss: r_t = E(theta_{t-1}) - E(theta_t). This measures improvement in world model prediction or compression.

In practice

• Use sealed, fixed audit panels to prevent reward gaming.
• Avoid clipping intrinsic rewards to maintain Goodhart resistance.
• Implement standard release defenses for reusable audit panels.

Topics

• Intrinsic Motivation
• Goodhart's Law
• Compression Progress
• Sealed Audit
• Reinforcement Learning
• Lean 4 Mechanization

Best for: Research Scientist, AI Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.