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

• URL: http://arxiv.org/abs/2605.09419v1
• Date: Sun, 10 May 2026 08:41:20 GMT
• Title: From Passive Reuse to Active Reasoning: Grounding Large Language Models for Neuro-Symbolic Experience Replay
• Abstract summary: We propose Neuro-Symbolic Experience Replay (NSER), a framework that transforms experience replay into an active engine for knowledge construction.<n>NSER addresses the incompatibility between linguistic reasoning and numerical optimization through a novel neuro-symbolic grounding pipeline.
• Score: 31.92429617798169
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: While experience replay is essential for data efficiency in reinforcement learning (RL), standard methods treat the replay buffer as a passive memory system, prioritizing samples based on numerical prediction errors rather than their semantic significance. This approach stands in contrast to human learning, which accelerates mastery by actively abstracting fragmented experiences into behavioral rules. To bridge this gap, we propose Neuro-Symbolic Experience Replay (NSER), a framework that transforms experience replay from a passive sample reuse mechanism into an active engine for knowledge construction. Specifically, NSER addresses the incompatibility between linguistic reasoning and numerical optimization through a novel neuro-symbolic grounding pipeline. It leverages Large Language Models (LLMs) in a zero-shot manner to induce candidate behavioral rules from accumulated trajectories, grounds these insights into differentiable first-order logic representations, and utilizes the resulting symbolic structures to dynamically reweight the replay distribution. By allowing abstract knowledge to directly shape policy optimization, NSER achieves consistent superior sample efficiency and convergence speed across reactive, rule-based, and procedural benchmarks.

Related papers

• Neural Symbollic Regression Using Deep Learning and Sparse Modelling [0.0]
  Symbolic Regression seeks to find succinct mathematical expressions that represent fundamental relationships within data.<n>Traditional methods like Genetic Programming face challenges with scalability and are highly sensitive to noise.<n>We present a Neural Symbolic Regression framework that treats neural networks as functional preconditioners for symbolic discovery.
  arXiv  Detail & Related papers  (2026-09-01T11:46:28Z)
• Probe-EM: Targeted Neuron Tracing via Training-Free Semantic Verification [13.282533076475383]
  We propose a training-free, targeted neuron tracing framework for processing terabyte- or petabyte-scale electron microscopy data.<n>Specifically, we introduce a skeleton-guided Heuristic Spatial Search paradigm that leverages geometric priors to iteratively reconstruct neuronal morphologies.<n>We further develop a Dimension-Aware Semantic Verification strategy built upon the foundation model NeuroSAM 2.
  arXiv  Detail & Related papers  (2026-07-06T05:56:26Z)
• LANPO: Bootstrapping Language and Numerical Feedback for Reinforcement Learning in LLMs [73.27182315028021]
  LANPO is a framework that cleanly separates the roles of feedback: language guides exploration, while numerical rewards drive optimization.<n>Our work provides a robust method for integrating historical experiences into the LLM RL loop, creating more effective and data-efficient learning agents.
  arXiv  Detail & Related papers  (2025-10-18T15:51:19Z)
• Sample-Efficient Online Learning in LM Agents via Hindsight Trajectory Rewriting [92.57796055887995]
  We introduce ECHO, a prompting framework that adapts hindsight experience replay from reinforcement learning for language model agents.<n> ECHO generates optimized trajectories for alternative goals that could have been achieved during failed attempts.<n>We evaluate ECHO on stateful versions of XMiniGrid, a text-based navigation and planning benchmark, and PeopleJoinQA, a collaborative information-gathering enterprise simulation.
  arXiv  Detail & Related papers  (2025-10-11T18:11:09Z)
• Learning Distinguishable Representations in Deep Q-Networks for Linear Transfer [0.9558392439655014]
  We propose a novel deep Q-learning approach that introduces a regularization term to reduce positive correlations between feature representation of states.<n>We demonstrate the efficacy of our approach in improving transfer learning performance and thereby reducing computational overhead.
  arXiv  Detail & Related papers  (2025-09-29T15:44:35Z)
• ReLUs Are Sufficient for Learning Implicit Neural Representations [17.786058035763254]
  We revisit the use of ReLU activation functions for learning implicit neural representations. Inspired by second order B-spline wavelets, we incorporate a set of simple constraints to the ReLU neurons in each layer of a deep neural network (DNN) We demonstrate that, contrary to popular belief, one can learn state-of-the-art INRs based on a DNN composed of only ReLU neurons.
  arXiv  Detail & Related papers  (2024-06-04T17:51:08Z)
• Provable Reward-Agnostic Preference-Based Reinforcement Learning [61.39541986848391]
  Preference-based Reinforcement Learning (PbRL) is a paradigm in which an RL agent learns to optimize a task using pair-wise preference-based feedback over trajectories. We propose a theoretical reward-agnostic PbRL framework where exploratory trajectories that enable accurate learning of hidden reward functions are acquired.
  arXiv  Detail & Related papers  (2023-05-29T15:00:09Z)
• Contrastive-Signal-Dependent Plasticity: Self-Supervised Learning in Spiking Neural Circuits [61.94533459151743]
  This work addresses the challenge of designing neurobiologically-motivated schemes for adjusting the synapses of spiking networks. Our experimental simulations demonstrate a consistent advantage over other biologically-plausible approaches when training recurrent spiking networks.
  arXiv  Detail & Related papers  (2023-03-30T02:40:28Z)
• Selective Memory Recursive Least Squares: Recast Forgetting into Memory in RBF Neural Network Based Real-Time Learning [2.31120983784623]
  In radial basis function neural network (RBFNN) based real-time learning tasks, forgetting mechanisms are widely used. This paper proposes a real-time training method named selective memory recursive least squares (SMRLS) in which the classical forgetting mechanisms are recast into a memory mechanism. With SMRLS, the input space of the RBFNN is evenly divided into a finite number of partitions and a synthesized objective function is developed using synthesized samples from each partition.
  arXiv  Detail & Related papers  (2022-11-15T05:29:58Z)
• Learning Bayesian Sparse Networks with Full Experience Replay for Continual Learning [54.7584721943286]
  Continual Learning (CL) methods aim to enable machine learning models to learn new tasks without catastrophic forgetting of those that have been previously mastered. Existing CL approaches often keep a buffer of previously-seen samples, perform knowledge distillation, or use regularization techniques towards this goal. We propose to only activate and select sparse neurons for learning current and past tasks at any stage.
  arXiv  Detail & Related papers  (2022-02-21T13:25:03Z)
• PredRNN: A Recurrent Neural Network for Spatiotemporal Predictive Learning [109.84770951839289]
  We present PredRNN, a new recurrent network for learning visual dynamics from historical context. We show that our approach obtains highly competitive results on three standard datasets.
  arXiv  Detail & Related papers  (2021-03-17T08:28:30Z)
• Learning to Sample with Local and Global Contexts in Experience Replay Buffer [135.94190624087355]
  We propose a new learning-based sampling method that can compute the relative importance of transition. We show that our framework can significantly improve the performance of various off-policy reinforcement learning methods.
  arXiv  Detail & Related papers  (2020-07-14T21:12:56Z)
• Automatic Recall Machines: Internal Replay, Continual Learning and the Brain [104.38824285741248]
  Replay in neural networks involves training on sequential data with memorized samples, which counteracts forgetting of previous behavior caused by non-stationarity. We present a method where these auxiliary samples are generated on the fly, given only the model that is being trained for the assessed objective. Instead the implicit memory of learned samples within the assessed model itself is exploited.
  arXiv  Detail & Related papers  (2020-06-22T15:07:06Z)
• Memory Augmented Neural Model for Incremental Session-based Recommendation [36.33193124174747]
  We show that existing neural recommenders can be used in incremental Session-based Recommendation scenarios. We propose a general framework called Memory Augmented Neural model (MAN) MAN augments a base neural recommender with a continuously queried and updated nonparametric memory.
  arXiv  Detail & Related papers  (2020-04-28T19:07:20Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.