Neuro-Symbolic Experience Replay: Grounding LLMs for Active Reasoning in RL
Researchers have introduced Neuro-Symbolic Experience Replay (NSER), a novel framework designed to enhance data efficiency in reinforcement learning (RL). Unlike standard methods that treat replay buffers as passive memory systems prioritizing samples based on numerical prediction errors, NSER transforms experience replay into an active engine for knowledge construction. This approach mirrors human learning by abstracting fragmented experiences into behavioral rules. The framework addresses the incompatibility between linguistic reasoning and numerical optimization through a neuro-symbolic grounding pipeline. It leverages Large Language Models (LLMs) in a zero-shot manner to induce candidate behavioral rules from accumulated trajectories. These insights are then grounded into differentiable first-order logic representations, which dynamically reweight the replay distribution. By allowing abstract knowledge to directly shape policy optimization, NSER demonstrates superior sample efficiency and convergence speed across reactive, rule-based, and procedural benchmarks. This development marks a significant step in integrating symbolic AI with neural networks to improve machine learning performance.
Wire timeline
Neuro-Symbolic Experience Replay: Grounding LLMs for Active Reasoning in RL
Researchers have introduced Neuro-Symbolic Experience Replay (NSER), a novel framework designed to enhance data efficiency in reinforcement learning (RL). Unlike standard methods that treat replay buffers as passive memory systems prioritizing samples based on numerical prediction errors, NSER transforms experience replay into an active engine for knowledge construction. This approach mirrors human learning by abstracting fragmented experiences into behavioral rules. The framework addresses the incompatibility between linguistic reasoning and numerical optimization through a neuro-symbolic grounding pipeline. It leverages Large Language Models (LLMs) in a zero-shot manner to induce candidate behavioral rules from accumulated trajectories. These insights are then grounded into differentiable first-order logic representations, which dynamically reweight the replay distribution. By allowing abstract knowledge to directly shape policy optimization, NSER demonstrates superior sample efficiency and convergence speed across reactive, rule-based, and procedural benchmarks. This development marks a significant step in integrating symbolic AI with neural networks to improve machine learning performance.
cs.AI updates on arXiv.org