Counterfactual Regret for Reflective Agents with Lean-Verified Guarantees

Key points

• However, existing guarantees for policy selection and action generation do not by themselves establish when local reflection yields long-term performance guarantees for the complete agent under resource constraints.1
• In this paper, we address this gap by deriving sufficient conditions for sublinear counterfactual regret from local generation and deployment guarantees, while retaining the native controller.1
• Specifically, we establish a resource-sensitive transfer that bounds full-state deployment discrepancies along actual execution paths and preserves the own-history laws of both learner and comparator.1
• To verify these guarantees, we formalize the conditional proof chain for all thirteen numbered theoretical results in Lean, including a native instance with repeated policy growth and imperfect validation.1

Sources (1)

• [1]Counterfactual Regret for Reflective Agents with Lean-Verified Guarantees
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 10, 10:22 AM

  Reflective language agents reuse feedback and memory to adapt across tasks without updating model weights.

  However, existing guarantees for policy selection and action generation do not by themselves establish when local reflection yields long-term performance guarantees for the complete agent under resource constraints.

Extractive summary: sentences quoted from the sources.