From Passive Reuse to Active Reasoning: Grounding Large Language Models for Neuro-Symbolic Experience Replay

Researchers introduce Neuro-Symbolic Experience Replay (NSER), a framework that enhances reinforcement learning by combining Large Language Models with symbolic logic to transform passive memory buffers into active knowledge construction systems. The approach grounds LLM-generated behavioral rules into differentiable logic representations, enabling more efficient policy optimization across multiple benchmark environments.

The research addresses a fundamental inefficiency in reinforcement learning systems: standard experience replay buffers passively store and sample experiences based on numerical prediction errors, ignoring semantic meaning. NSER bridges this gap by introducing a three-stage pipeline where LLMs extract behavioral rules from accumulated trajectories in a zero-shot manner, convert these linguistic insights into first-order logic representations, and use the resulting symbolic structures to dynamically reweight which samples the agent learns from. This neuro-symbolic approach mirrors human learning more closely, where abstract rule discovery accelerates skill acquisition beyond raw data repetition.

The framework addresses a longstanding challenge in AI systems: reconciling the strengths of neural networks (pattern recognition, numerical optimization) with symbolic reasoning (interpretability, logical consistency). Prior experience replay methods treat all data equally or prioritize based on surprise, missing opportunities to learn from semantically meaningful patterns. NSER's integration of LLMs enables extraction of high-level insights that inform lower-level policy optimization, creating a feedback loop between abstraction and execution.

For the AI research community, this work has implications for sample efficiency in reinforcement learning—a critical bottleneck in robotics and autonomous systems where collecting real-world experience is expensive. The demonstrated improvements across reactive, rule-based, and procedural tasks suggest the approach generalizes beyond narrow domains. The zero-shot use of LLMs also reduces the need for task-specific training, potentially lowering barriers to adoption.

Future development hinges on scaling these methods to complex environments and validating whether symbolic grounding maintains interpretability benefits while preserving optimization performance. The interaction between LLM-generated rules and continuous policy learning warrants deeper investigation.

• →NSER transforms experience replay from passive memory into active knowledge construction by grounding LLM-generated behavioral rules into symbolic logic.
• →The framework achieves superior sample efficiency and convergence speed by using abstract knowledge to dynamically reweight replay distributions.
• →The approach bridges neural and symbolic AI by combining LLM reasoning with differentiable first-order logic for policy optimization.
• →Results demonstrate consistent improvements across reactive, rule-based, and procedural benchmarks, indicating broad applicability.
• →Zero-shot LLM application reduces task-specific engineering requirements, potentially accelerating adoption in robotics and autonomous systems.