arxiv: 2604.27003 · v1 · submitted 2026-04-29 · 💻 cs.LG · cs.AI

Recognition: unknown

When Continual Learning Moves to Memory: A Study of Experience Reuse in LLM Agents

Qisheng Hu , Quanyu Long , Wenya Wang

Authors on Pith no claims yet

Pith reviewed 2026-05-07 13:33 UTC · model grok-4.3

classification 💻 cs.LG cs.AI

keywords continual learningLLM agentsexternal memoryexperience reusememory representationretrieval designsequential taskstransfer and forgetting

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The pith

External memory in LLM agents does not eliminate continual learning challenges but relocates them to memory representation and retrieval design.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper tests whether LLM agents can achieve continual learning by storing experiences in external memory instead of changing model parameters. It finds that limited context windows force old and new experiences to compete at retrieval time, recreating the stability-plasticity tension in a new form. Experiments across sequential tasks separate two design choices: how each experience is represented and how the collection is organized for access. Abstract procedural summaries transfer more reliably than full trajectories, yet some organizations that aid new tasks increase forgetting of earlier ones. This suggests that external memory reframes rather than removes the continual-learning problem.

Core claim

Memory-augmented LLM agents appear to sidestep the stability-plasticity dilemma by accumulating experience externally rather than updating parameters. Under limited context, however, retrieval pits old and new experiences against each other, moving the bottleneck to memory access. A (k,v) framework isolates representation from organization. Sequential-task runs in ALFWorld and BabyAI show that abstract procedural memories transfer more reliably than detailed trajectories, negative transfer hits hard cases hardest, and finer-grained organization can strengthen forward transfer while worsening forgetting.

What carries the argument

The (k,v) framework that separates how individual experiences are represented from how the memory store is organized for retrieval.

If this is right

Abstract procedural memories support more reliable transfer across tasks than storing complete trajectories.
Negative transfer from earlier experiences reduces performance most on the hardest subsequent tasks.
Memory organizations that improve forward transfer can simultaneously increase forgetting of prior knowledge.
The performance bottleneck for these agents shifts from parameter updates to choices about how memories are stored and retrieved.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

Retrieval algorithms that actively filter or compress experiences may be needed to reduce competition inside fixed context windows.
The observed trade-offs between transfer gains and forgetting may appear in other sequential decision settings outside the two environments tested.
Task-specific tuning of representation granularity could be required rather than a single universal memory organization.

Load-bearing premise

The (k,v) framework adequately isolates the main design axes of memory representation and organization, and results from sequential tasks in ALFWorld and BabyAI extend beyond those two settings.

What would settle it

An experiment in which unlimited context or perfect retrieval removes all measurable competition between old and new experiences, or in which no variation in representation or organization produces differences in transfer or forgetting, would falsify the claim that the continual-learning problem merely relocates to memory design.

Figures

Figures reproduced from arXiv: 2604.27003 by Qisheng Hu, Quanyu Long, Wenya Wang.

**Figure 1.** Figure 1: Evaluation protocol for the A → B direction. The agent first learns a stream of Task A instances from empty memory, then continues with a stream of Task B instances while reusing the accumulated memory. and continue accumulating new experience online. This makes memory-augmented agents especially relevant to continual learning (CL), whose core setting assumes sequentially arriving data and ongoing adaptat… view at source ↗

**Figure 3.** Figure 3: ∆RR/∆NL decomposition (A→B). Under Raw, the hard subset (purple) suffers more than the easy subset (teal), especially on ALFWorld. Insight memory largely reduces this asymmetry. Scratch (within-task) Cross-task (A memory) 65.0 67.5 70.0 72.5 75.0 77.5 80.0 Success Rate on Task B (%) 80% 71% 66% 72% ALFWorld Scratch (within-task) Cross-task (A memory) 46 48 50 52 54 54% 46% 46% 55% BabyAI Raw Insight view at source ↗

**Figure 4.** Figure 4: Within-task vs. cross-task performance on view at source ↗

**Figure 5.** Figure 5: BWT by representation. On BabyAI, Insight view at source ↗

**Figure 6.** Figure 6: Visual comparison of Cond-Agg, Cond-Ind, view at source ↗

**Figure 8.** Figure 8: BabyAI: FWT by direction, contrasting unit view at source ↗

**Figure 9.** Figure 9: ∆RR/∆NL diagnostic for A→B (Study 2). On BabyAI, Cond-Ind shows broad improvement across both subsets (∆RR = +19.4, ∆NL = +10.8), while on ALFWorld Cond-Agg yields positive diagnostics. that ALFWorld tasks rely on coherent multi-step strategies (e.g., find→take→clean→place), so fragmenting those strategies into individually indexed insights may weaken the structure that is preserved when they are bundled.… view at source ↗

**Figure 11.** Figure 11: Retrieval diversity analysis (BabyAI, within-task setting). view at source ↗

**Figure 12.** Figure 12: ALFWorld cumulative success rate (training set). Raw A view at source ↗

**Figure 13.** Figure 13: ALFWorld Task B: RR/NL dynamics over training. RR remains stable; the NL gap is the primary driver view at source ↗

read the original abstract

Memory-augmented LLM agents offer an appealing shortcut to continual learning: rather than updating model parameters, they accumulate experience in external memory, seemingly sidestepping the stability-plasticity dilemma of parametric learning. We show that this challenge does not disappear but resurfaces at the memory level. Under a limited context window, old and new experiences compete during retrieval, relocating the continual-learning bottleneck from parameter updates to memory access. To study this phenomenon, we introduce a (k,v) framework that disentangles two fundamental design axes of external memory: how experience is represented and how it is organized for retrieval. Across sequential-task experiments in ALFWorld and BabyAI, we find that abstract procedural memories transfer more reliably than detailed trajectories, while negative transfer disproportionately harms the hard cases. Moreover, finer-grained memory organization is not universally beneficial: designs that yield strong forward transfer can simultaneously induce severe forgetting. Together, these results reveal that external memory does not resolve the continual-learning problem; it reshapes it into a problem of memory representation and retrieval design.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

External memory shifts the continual learning problem to representation and retrieval design in LLM agents, with some plausible patterns but thin evidence on causality and generality.

read the letter

The paper's core point is that memory-augmented LLM agents still run into stability-plasticity trade-offs, just now at the level of what gets stored and how it gets pulled back under a limited context window. They introduce a (k,v) split to separate representation choices from organization choices, then test sequential tasks in ALFWorld and BabyAI. Abstract procedural memories transfer more reliably than detailed trajectories, negative transfer hurts the harder cases more, and finer-grained organization can improve forward transfer while also increasing forgetting of prior experience. These observations are the main new empirical bits; they give concrete examples of how memory design can create its own forgetting mechanisms rather than just inheriting the parametric ones. That framing is useful for anyone trying to build agents that reuse experience over long horizons. The work does a decent job flagging that simply dumping everything into memory is not a free lunch. The soft spots are mostly around support and scope. The abstract gives no numbers, no ablation tables, no statistical tests, and no controls, so the size and reliability of the reported transfer and forgetting effects are hard to judge from what's here. Two environments also leave open whether the negative transfer on hard cases is a general feature or tied to the state spaces in ALFWorld and BabyAI. The (k,v) framework is presented as disentangling the axes, but if context-window mechanics couple representation and retrieval in practice, the experiments may not cleanly isolate which factor drives the competition. This is worth a serious referee for groups working on agent memory architectures. The topic is timely, the patterns are worth checking, and the paper would benefit from added metrics and broader testing rather than being dismissed outright. I would not cite it yet but would want to see the full results in review.

Referee Report

2 major / 2 minor

Summary. The paper claims that external memory in LLM agents does not resolve the continual-learning problem but relocates the stability-plasticity dilemma to the level of memory representation and retrieval design. It introduces a (k,v) framework to disentangle experience representation from retrieval organization and reports experimental findings from sequential tasks in ALFWorld and BabyAI: abstract procedural memories transfer more reliably than detailed trajectories, negative transfer disproportionately affects hard cases, and finer-grained memory organization can produce strong forward transfer at the cost of severe forgetting.

Significance. If the results hold, this work is significant for highlighting that memory augmentation merely reshapes rather than eliminates continual-learning challenges in LLM agents, with concrete implications for designing robust memory systems. The empirical observations across two environments and the structured (k,v) lens for analyzing representation vs. organization trade-offs provide a useful foundation for future agent research. The paper earns credit for its empirical focus on transfer and forgetting behaviors in named benchmarks.

major comments (2)

[§3 The (k,v) Framework] §3 The (k,v) Framework: The central claim that external memory 'reshapes' the continual-learning problem into one of representation and retrieval design rests on the assertion that the framework 'disentangles two fundamental design axes.' However, the manuscript provides no evidence or ablation that representation choices (e.g., abstract procedural vs. trajectory) and organization choices (e.g., retrieval granularity) can be varied independently; shared context-window mechanics and prompting may couple them, so observed differences in transfer and forgetting cannot be cleanly attributed to the intended axes.
[§4 Experiments] §4 Experiments: The reported effects on forward transfer, negative transfer, and forgetting lack quantitative metrics, statistical tests, ablation details, or controls for confounds. This is load-bearing for the reshaping claim because the abstract states that 'abstract procedural memories transfer more reliably' and 'finer-grained memory organization is not universally beneficial,' yet without these elements it is impossible to assess effect sizes or rule out environment-specific artifacts from ALFWorld and BabyAI state spaces.

minor comments (2)

The (k,v) notation is introduced without an early formal definition or illustrative diagram, which reduces clarity when the framework is used to interpret later results.
[Abstract] The abstract and experimental descriptions would benefit from explicit baselines and comparison conditions to make the transfer and forgetting claims easier to interpret at a glance.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive and detailed feedback. We address each major comment below and will incorporate revisions to improve clarity and rigor.

read point-by-point responses

Referee: §3 The (k,v) Framework: The central claim that external memory 'reshapes' the continual-learning problem into one of representation and retrieval design rests on the assertion that the framework 'disentangles two fundamental design axes.' However, the manuscript provides no evidence or ablation that representation choices (e.g., abstract procedural vs. trajectory) and organization choices (e.g., retrieval granularity) can be varied independently; shared context-window mechanics and prompting may couple them, so observed differences in transfer and forgetting cannot be cleanly attributed to the intended axes.

Authors: We thank the referee for highlighting this point. The (k,v) framework conceptually separates representation (k: e.g., abstract procedural vs. detailed trajectory encoding of experiences) from organization (v: e.g., retrieval granularity and indexing). Our experiments vary one axis while holding the other fixed across conditions in both ALFWorld and BabyAI, with prompting adapted consistently to the memory content. While the shared context window is a common constraint, the distinct outcomes in transfer and forgetting are attributable to the targeted changes in content and structure. We will revise §3 to explicitly document these independent manipulations, add ablation tables, and include diagrams showing the design variations. revision: yes
Referee: §4 Experiments: The reported effects on forward transfer, negative transfer, and forgetting lack quantitative metrics, statistical tests, ablation details, or controls for confounds. This is load-bearing for the reshaping claim because the abstract states that 'abstract procedural memories transfer more reliably' and 'finer-grained memory organization is not universally beneficial,' yet without these elements it is impossible to assess effect sizes or rule out environment-specific artifacts from ALFWorld and BabyAI state spaces.

Authors: We agree that stronger quantification is needed to support the claims. The current manuscript emphasizes illustrative results and qualitative patterns from sequential tasks, but we will expand §4 with quantitative tables (success rates, forward transfer deltas, forgetting rates with standard deviations), statistical tests (e.g., paired t-tests or Wilcoxon where appropriate), full ablation details on representation and organization choices, and explicit discussion of potential confounds such as task ordering and environment state-space differences. These additions will enable assessment of effect sizes and robustness. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical observations from benchmark experiments

full rationale

The paper introduces a (k,v) framework as an analytical lens and reports findings on memory representation and retrieval from sequential-task experiments in ALFWorld and BabyAI. No derivations, fitted parameters renamed as predictions, or self-citations appear in the provided text; the central claim that external memory relocates the continual-learning bottleneck rests on direct experimental comparisons of transfer, forgetting, and negative transfer rather than any reduction to inputs by construction. The analysis is therefore self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 1 invented entities

The central claim rests on the domain assumption that limited context causes retrieval competition and that the (k,v) framework captures the essential design axes; no free parameters or invented entities with independent evidence are stated.

axioms (2)

domain assumption Limited context window causes old and new experiences to compete during retrieval
Invoked to explain why the continual-learning bottleneck relocates to memory access
domain assumption The (k,v) framework disentangles representation and organization as the two fundamental design axes
Introduced to structure the study of memory-augmented agents

invented entities (1)

(k,v) framework no independent evidence
purpose: To separate how experience is represented from how it is organized for retrieval
New conceptual tool introduced in the paper to analyze memory design choices

pith-pipeline@v0.9.0 · 5482 in / 1344 out tokens · 45923 ms · 2026-05-07T13:33:29.105355+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

4 extracted references · 3 canonical work pages · 2 internal anchors

[1]

Remember Me, Refine Me: A Dynamic Procedural Memory Framework for Experience-Driven Agent Evolution

Improving language models by retrieving from trillions of tokens. InInternational conference on machine learning, pages 2206–2240. PMLR. Zouying Cao, Jiaji Deng, Li Yu, Weikang Zhou, Zhaoyang Liu, Bolin Ding, and Hai Zhao. 2025. Re- member me, refine me: A dynamic procedural mem- ory framework for experience-driven agent evolution. arXiv preprint arXiv:25...

work page internal anchor Pith review Pith/arXiv arXiv 2025
[2]

Clin: A continually learning language agent for rapid task adaptation and generalization

Clin: A continually learning language agent for rapid task adaptation and generalization.arXiv preprint arXiv:2310.10134. Arun Mallya and Svetlana Lazebnik. 2018. Packnet: Adding multiple tasks to a single network by iterative pruning. InProceedings of the IEEE conference on Computer Vision and Pattern Recognition, pages 7765–7773. Kolby Nottingham, Bodhi...

work page arXiv 2018
[3]

MemSkill: Learning and Evolving Memory Skills for Self-Evolving Agents

Memskill: Learning and evolving mem- ory skills for self-evolving agents.arXiv preprint arXiv:2602.02474. Zeyu Zhang, Quanyu Dai, Xiaohe Bo, Chen Ma, Rui Li, Xu Chen, Jieming Zhu, Zhenhua Dong, and Ji-Rong Wen. 2025. A survey on the memory mechanism of large language model-based agents.ACM Transac- tions on Information Systems, 43(6):1–47. Andrew Zhao, Da...

work page internal anchor Pith review arXiv 2025
[4]

open the pur- ple door

(full episode, typically 20–50 turns) Insight entry(ALFWorld, same task). Key: same task instruction as Raw. Content: ∼3 abstract insights distilled by the LLM. For example: Insight 1.Check countertops and tables first, as they are the most common locations for portable objects like bowls and cups. Insight 2.If the target object is not visible, sys- temat...