Scalable Weak-to-Strong Verifier Hardening for Agent Environments

Key points

• Verifiers can be attacked into awarding full credit to cheating agents without completing the intended task, causing inaccurate evaluation and misalignment in training.1
• To this end, we ask whether today's model with scaled compute can automatically harden agent environments to prevent potentially stronger future models from reward hacking.1
• We introduce a scalable, fully automatic approach to weak-to-strong verifier hardening.1
• We apply this loop to Terminal Bench, KernelBench, and SETA, substantially reducing attack success across these agent environments.1

Sources (2)

• [1]Scalable Weak-to-Strong Verifier Hardening for Agent Environments
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 11, 09:10 PM

  Agent environments are essential for large language model (LLM) development.

  Verifiers can be attacked into awarding full credit to cheating agents without completing the intended task, causing inaccurate evaluation and misalignment in training.

• [2]Scalable Weak-to-Strong Verifier Hardening for Agent Environments
  Hugging Face Daily Papers · Oct 11, 12:00 AM · same content

Extractive summary: sentences quoted from the sources.