Steering Black-Box Large Language Model Agents with Pitfall Anticipation World Model

Key points

• This raises a key question: what a world model should predict and when should it be invoked for agentic tasks?1
• Motivated by this, we introduce Pitfall Anticipation World Model (PAWM), which selectively intervenes when a potential pitfall arises and predicts the impending failure to steer the agent away from it.1
• PAWM is trained in two stages: Supervised Fine-Tuning (SFT) on synthetic data labeled with agent pitfalls, followed by GRPO co-training with a black-box LLM to further optimize it in the target environment.1
• These results demonstrate that PAWM provides an effective form of world model guidance for black-box LLM agents.1

Sources (1)

• [1]Steering Black-Box Large Language Model Agents with Pitfall Anticipation World Model
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 21, 03:20 AM

  Frontier black-box Large Language Model (LLM) agents are proficient at long-horizon reasoning and precise action generation.

  This raises a key question: what a world model should predict and when should it be invoked for agentic tasks?

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