Beyond Scalar Rewards: Dense Feedback for LLM Policy Synthesis in Sequential Social Dilemmas
arXiv:2603.19453v2 Announce Type: replace Abstract: We study LLM policy synthesis: using a language model to iteratively generate programmatic agent policies for multi-agent environments. Rather than training neural policies via reinforcement learning, our framework prompts an LLM to produce Python policy functions, evaluates them in self-play, and refines them using performance feedback across iterations. We investigate feedback engineering (the design of what evaluation information is shown to the LLM during refinement) comparing sparse feedback (scalar reward only) against dense feedback (r
The proliferation of increasingly capable LLMs makes exploring advanced policy synthesis methods critical for developing more autonomous and robust AI agents.
This research provides a pathway to more sophisticated and adaptable AI behaviors, moving beyond simple reward functions to leverage richer feedback for complex decision-making in multi-agent systems.
The focus shifts from purely reinforcement learning for neural networks to leveraging LLMs for programmatic policy generation and iterative refinement, potentially accelerating agent development and improving interpretability.
- · AI researchers
- · LLM developers
- · Multi-agent system developers
- · Gaming and simulation industries
- · Traditional RL policy synthesis methods
LLMs gain a new and powerful application in generating and refining complex programmatic agent policies.
The development of more sophisticated autonomous AI agents accelerates, capable of understanding and adapting to complex environments more effectively.
This could lead to a ' Cambrian explosion' of novel AI agent behaviors and applications in various domains, from robotics to social simulations.
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