In-Memory Learning: A Declarative Learning Framework for Large Language Models

• URL: http://arxiv.org/abs/2403.02757v1
• Date: Tue, 5 Mar 2024 08:25:11 GMT
• Title: In-Memory Learning: A Declarative Learning Framework for Large Language Models
• Authors: Bo Wang, Tianxiang Sun, Hang Yan, Siyin Wang, Qingyuan Cheng, Xipeng Qiu
• Abstract summary: We propose a novel learning framework that allows agents to align with their environment without relying on human-labeled data. This entire process transpires within the memory components and is implemented through natural language. We demonstrate the effectiveness of our framework and provide insights into this problem.
• Score: 56.62616975119192
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The exploration of whether agents can align with their environment without relying on human-labeled data presents an intriguing research topic. Drawing inspiration from the alignment process observed in intelligent organisms, where declarative memory plays a pivotal role in summarizing past experiences, we propose a novel learning framework. The agents adeptly distill insights from past experiences, refining and updating existing notes to enhance their performance in the environment. This entire process transpires within the memory components and is implemented through natural language, so we character this framework as In-memory Learning. We also delve into the key features of benchmarks designed to evaluate the self-improvement process. Through systematic experiments, we demonstrate the effectiveness of our framework and provide insights into this problem.

Related papers

• Rethinking Memory Mechanisms of Foundation Agents in the Second Half: A Survey [211.01908189012184]
  Memory, with hundreds of papers released this year, emerges as the critical solution to fill the utility gap.<n>We provide a unified view of foundation agent memory along three dimensions.<n>We then analyze how memory is instantiated and operated under different agent topologies.
  arXiv  Detail & Related papers  (2026-01-14T07:38:38Z)
• The AI Hippocampus: How Far are We From Human Memory? [77.04745635827278]
  Implicit memory refers to the knowledge embedded within the internal parameters of pre-trained transformers.<n>Explicit memory involves external storage and retrieval components designed to augment model outputs with dynamic, queryable knowledge representations.<n>Agentic memory introduces persistent, temporally extended memory structures within autonomous agents.
  arXiv  Detail & Related papers  (2026-01-14T03:24:08Z)
• Memento 2: Learning by Stateful Reflective Memory [4.7052412989773975]
  We study continual learning in large language model (LLM) based agents that integrate episodic memory with reinforcement learning.<n>We focus on reflection, the ability of an agent to revisit past experience and adjust how it selects future actions.<n>We introduce the Stateful Reflective Decision Process (SRDP), in which an agent maintains and updates episodic memory and alternates between writing new experiences to memory and reading relevant cases to guide decisions.
  arXiv  Detail & Related papers  (2025-12-27T22:15:03Z)
• Continual Learning of Domain Knowledge from Human Feedback in Text-to-SQL [9.964158093998277]
  We introduce a framework for continual learning from human feedback in text-to-aware schemas and distilled schemas.<n>We show that memory-augmented agents, particularly the Procedural Agent, achieve significant accuracy gains and error reduction by leveraging human-in-the-loop feedback.
  arXiv  Detail & Related papers  (2025-11-10T05:29:10Z)
• The Imperfect Learner: Incorporating Developmental Trajectories in Memory-based Student Simulation [55.722188569369656]
  This paper introduces a novel framework for memory-based student simulation.<n>It incorporates developmental trajectories through a hierarchical memory mechanism with structured knowledge representation.<n>In practice, we implement a curriculum-aligned simulator grounded on the Next Generation Science Standards.
  arXiv  Detail & Related papers  (2025-11-08T08:05:43Z)
• Learning from Supervision with Semantic and Episodic Memory: A Reflective Approach to Agent Adaptation [11.819481846962447]
  We investigate how agents built on pretrained large language models can learn target classification functions from labeled examples without parameter updates.<n>Our framework uses episodic memory to store instance-level critiques and distill these into reusable, task-level guidance.<n>Our findings highlight the promise of memory-driven, reflective learning for building more adaptive and interpretable LLM agents.
  arXiv  Detail & Related papers  (2025-10-22T17:58:03Z)
• Fuzzy, Symbolic, and Contextual: Enhancing LLM Instruction via Cognitive Scaffolding [3.553493344868413]
  We study how prompt-level inductive biases influence the cognitive behavior of large language models (LLMs) in instructional dialogue.<n>We introduce a symbolic scaffolding method paired with a short-term memory schema designed to promote adaptive, structured reasoning.<n>Preliminary results show that our full system consistently outperforms baseline variants.
  arXiv  Detail & Related papers  (2025-08-28T20:46:13Z)
• What to Do Next? Memorizing skills from Egocentric Instructional Video [43.59787683244105]
  We present a novel task, interactive action planning, and propose an approach that combines topological affordance memory with transformer architecture.<n>Our experimental results demonstrate that the proposed approach learns meaningful representations, resulting in improved performance and robust when action deviations occur.
  arXiv  Detail & Related papers  (2025-07-01T22:53:41Z)
• From Memories to Maps: Mechanisms of In-Context Reinforcement Learning in Transformers [2.4554686192257424]
  We train a transformer to in-context reinforcement learn in a distribution of planning tasks inspired by rodent behavior.<n>We characterize the learning algorithms that emerge in the model.<n>We find that memory may serve as a computational resource, storing both raw experience and cached computations to support flexible behavior.
  arXiv  Detail & Related papers  (2025-06-24T14:55:43Z)
• An Empirical Study of Federated Prompt Learning for Vision Language Model [50.73746120012352]
  This paper systematically investigates behavioral differences between language prompt learning and vision prompt learning.<n>We conduct experiments to evaluate the impact of various fl and prompt configurations, such as client scale, aggregation strategies, and prompt length.<n>We explore strategies for enhancing prompt learning in complex scenarios where label skew and domain shift coexist.
  arXiv  Detail & Related papers  (2025-05-29T03:09:15Z)
• Developmentally-plausible Working Memory Shapes a Critical Period for Language Acquisition [8.43537886261228]
  Large language models possess general linguistic abilities but acquire language less efficiently than humans. This study proposes a method for integrating the developmental characteristics of working memory during the critical period.
  arXiv  Detail & Related papers  (2025-02-07T09:58:58Z)
• Decorrelation-based Self-Supervised Visual Representation Learning for Writer Identification [10.55096104577668]
  We explore the decorrelation-based paradigm of self-supervised learning and apply the same to learning disentangled stroke features for writer identification. We show that the proposed framework outperforms the contemporary self-supervised learning framework on the writer identification benchmark. To the best of our knowledge, this work is the first of its kind to apply self-supervised learning for learning representations for writer verification tasks.
  arXiv  Detail & Related papers  (2024-10-02T11:43:58Z)
• Learning Symbolic Task Representation from a Human-Led Demonstration: A Memory to Store, Retrieve, Consolidate, and Forget Experiences [3.0501524254444767]
  We present a symbolic learning framework inspired by cognitive-like memory functionalities. Our main contribution is the formalisation of a framework that can be used to investigate different memorises for bootstrapping hierarchical knowledge representations.
  arXiv  Detail & Related papers  (2024-04-16T14:14:34Z)
• Analysis of the Memorization and Generalization Capabilities of AI Agents: Are Continual Learners Robust? [91.682459306359]
  In continual learning (CL), an AI agent learns from non-stationary data streams under dynamic environments. In this paper, a novel CL framework is proposed to achieve robust generalization to dynamic environments while retaining past knowledge. The generalization and memorization performance of the proposed framework are theoretically analyzed.
  arXiv  Detail & Related papers  (2023-09-18T21:00:01Z)
• RET-LLM: Towards a General Read-Write Memory for Large Language Models [53.288356721954514]
  RET-LLM is a novel framework that equips large language models with a general write-read memory unit. Inspired by Davidsonian semantics theory, we extract and save knowledge in the form of triplets. Our framework exhibits robust performance in handling temporal-based question answering tasks.
  arXiv  Detail & Related papers  (2023-05-23T17:53:38Z)
• Information-Theoretic Odometry Learning [83.36195426897768]
  We propose a unified information theoretic framework for learning-motivated methods aimed at odometry estimation. The proposed framework provides an elegant tool for performance evaluation and understanding in information-theoretic language.
  arXiv  Detail & Related papers  (2022-03-11T02:37:35Z)
• Learning What to Memorize: Using Intrinsic Motivation to Form Useful Memory in Partially Observable Reinforcement Learning [0.0]
  In order to learn in an ambiguous environment, an agent has to keep previous perceptions in a memory. In this study, we follow the idea of giving the control of the memory to the agent by allowing it to have memory-changing actions. This learning mechanism is supported by an intrinsic motivation to memorize rare observations that can help the agent to disambiguate its state in the environment.
  arXiv  Detail & Related papers  (2021-10-25T11:15:54Z)
• Self-training with Few-shot Rationalization: Teacher Explanations Aid Student in Few-shot NLU [88.8401599172922]
  We develop a framework based on self-training language models with limited task-specific labels and rationales. We show that the neural model performance can be significantly improved by making it aware of its rationalized predictions.
  arXiv  Detail & Related papers  (2021-09-17T00:36:46Z)
• Learning to Learn Variational Semantic Memory [132.39737669936125]
  We introduce variational semantic memory into meta-learning to acquire long-term knowledge for few-shot learning. The semantic memory is grown from scratch and gradually consolidated by absorbing information from tasks it experiences. We formulate memory recall as the variational inference of a latent memory variable from addressed contents.
  arXiv  Detail & Related papers  (2020-10-20T15:05:26Z)

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.