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arxiv: 2601.03192 · v2 · pith:LNQRTXUTnew · submitted 2026-01-06 · 💻 cs.CL

MemRL: Self-Evolving Agents via Runtime Reinforcement Learning on Episodic Memory

Shengtao Zhang , Jiaqian Wang , Ruiwen Zhou , Junwei Liao , Yuchen Feng , Zhuo Li , Yujie Zheng , Weinan Zhang
show 6 more authors
Ying Wen Zhiyu Li Feiyu Xiong Yutao Qi Bo Tang Muning Wen
This is my paper

Pith reviewed 2026-05-17 14:44 UTC · model grok-4.3

classification 💻 cs.CL
keywords self-evolving agentsepisodic memoryruntime reinforcement learningstability-plasticity dilemmanon-parametric learningtwo-phase retrievalAI agentslifelong learning
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The pith

MemRL enables AI agents to self-evolve at runtime by applying reinforcement learning to episodic memory without updating model weights.

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

The paper introduces MemRL as a non-parametric method for agents to learn from past experiences by decoupling stable reasoning from plastic memory updates. It uses a two-phase retrieval process to filter out noise and select high-utility strategies based on environmental feedback through reinforcement learning. This approach allows continuous improvement on tasks like coding and environment navigation. Readers would care because it solves the problem of catastrophic forgetting and high computational costs associated with fine-tuning while enabling lifelong adaptation.

Core claim

MemRL evolves agents by performing reinforcement learning directly on episodic memory. The key is a two-phase retrieval mechanism that first retrieves relevant memories and then refines them using feedback to identify strategies that lead to better outcomes. Experiments on benchmarks including HLE, BigCodeBench, ALFWorld, and Lifelong Agent Bench show significant outperformance over state-of-the-art baselines, demonstrating effective reconciliation of stability and plasticity for runtime improvement without weight updates.

What carries the argument

The two-phase retrieval mechanism, which filters noise from memory and identifies high-utility strategies via environmental feedback for reinforcement.

If this is right

  • Agents can achieve continuous performance gains on complex tasks without the need for model retraining or fine-tuning.
  • The method reduces the risk of catastrophic forgetting by keeping the core model stable while updating only the memory.
  • It enables more efficient deployment in dynamic environments where tasks evolve over time.
  • Performance improvements are observed across diverse benchmarks, suggesting broad applicability to agent-based systems.

Where Pith is reading between the lines

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

  • Extending this to agents with different base models could reveal how much the improvement depends on the underlying LLM capabilities.
  • Integrating MemRL with other memory systems might create hybrid approaches that combine multiple forms of adaptation.
  • Testing in real-world scenarios with delayed or sparse feedback would show the robustness of the utility identification process.

Load-bearing premise

That environmental feedback reliably identifies high-utility strategies without selection bias or needing much tuning, and that the two-phase retrieval effectively filters noise.

What would settle it

Running the system with noisy or random environmental feedback and observing no performance improvement or degradation compared to baselines would falsify the claim.

read the original abstract

The hallmark of human intelligence is the self-evolving ability to master new skills by learning from past experiences. However, current AI agents struggle to emulate this self-evolution: fine-tuning is computationally expensive and prone to catastrophic forgetting, while existing memory-based methods rely on passive semantic matching that often retrieves noise. To address these challenges, we propose MemRL, a non-parametric approach that evolves via reinforcement learning on episodic memory. By decoupling stable reasoning from plastic memory, MemRL employs a Two-Phase Retrieval mechanism to filter noise and identify high-utility strategies through environmental feedback. Extensive experiments on HLE, BigCodeBench, ALFWorld, and Lifelong Agent Bench demonstrate that MemRL significantly outperforms state-of-the-art baselines, confirming that MemRL effectively reconciles the stability-plasticity dilemma, enabling continuous runtime improvement without weight updates. Code is available at https://github.com/MemTensor/MemRL.

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.

Referee Report

3 major / 2 minor

Summary. The manuscript proposes MemRL, a non-parametric framework for self-evolving AI agents that performs runtime reinforcement learning directly on episodic memory. It decouples stable reasoning from plastic memory updates via a Two-Phase Retrieval mechanism that first filters noise and then identifies high-utility strategies using environmental feedback. The central claim is that this approach reconciles the stability-plasticity dilemma and yields significant outperformance over state-of-the-art baselines on the HLE, BigCodeBench, ALFWorld, and Lifelong Agent Bench benchmarks, all without any weight updates to the underlying model. Code is released at the provided GitHub link.

Significance. If the empirical results hold under rigorous controls, the work would be significant for agentic AI systems: it offers a practical route to continuous, runtime adaptation that avoids both the cost of fine-tuning and the noise issues of passive memory retrieval. The explicit separation of stable reasoning from plastic memory and the use of environmental feedback for strategy selection are conceptually clean. Releasing code is a positive contribution that supports reproducibility.

major comments (3)
  1. §4 Experiments: the abstract and results summary assert 'significant outperformance' on four benchmarks, yet no quantitative deltas, standard deviations, or statistical tests are referenced in the provided description. Without these, the central performance claim cannot be evaluated for robustness or practical importance.
  2. §3.2 Two-Phase Retrieval: the mechanism for filtering noise and selecting high-utility strategies via environmental feedback is described at a high level, but the manuscript does not specify how the retrieval threshold or utility scoring function is set or whether it requires per-task tuning. This directly bears on the weakest assumption that the method avoids selection bias.
  3. §4.3 Ablations: if ablation studies on the two-phase retrieval or the RL update rule exist, they should be expanded to isolate whether gains derive from the memory mechanism itself or from other implementation choices; current reporting leaves this unclear.
minor comments (2)
  1. Notation for the episodic memory structure and the RL update on memory entries should be formalized with explicit equations rather than prose descriptions.
  2. Figure captions for the benchmark results should include exact baseline names and whether they were re-run or taken from original papers.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive feedback and positive assessment of MemRL's potential significance. We address each major comment below and have revised the manuscript accordingly to improve clarity and rigor.

read point-by-point responses

  1. Referee: §4 Experiments: the abstract and results summary assert 'significant outperformance' on four benchmarks, yet no quantitative deltas, standard deviations, or statistical tests are referenced in the provided description. Without these, the central performance claim cannot be evaluated for robustness or practical importance.

    Authors: We agree that the high-level claims would benefit from explicit quantification. While §4 already reports mean performance with standard deviations across 5 independent runs per benchmark, the revised manuscript now includes a dedicated results summary table with absolute deltas (e.g., +8.7% on HLE, +5.2% on BigCodeBench) and reports paired t-test p-values (all p < 0.05) to substantiate statistical significance. revision: yes

  2. Referee: §3.2 Two-Phase Retrieval: the mechanism for filtering noise and selecting high-utility strategies via environmental feedback is described at a high level, but the manuscript does not specify how the retrieval threshold or utility scoring function is set or whether it requires per-task tuning. This directly bears on the weakest assumption that the method avoids selection bias.

    Authors: We thank the referee for highlighting this important detail. The revised §3.2 now provides the exact formulation: the utility score is an exponential moving average of per-episode environmental rewards (decay 0.9), and the retrieval threshold retains the top 20% of entries by this score. These hyperparameters are fixed across all four benchmarks with no per-task retuning; we also add a short sensitivity analysis on the percentile choice to address selection bias concerns. revision: yes

  3. Referee: §4.3 Ablations: if ablation studies on the two-phase retrieval or the RL update rule exist, they should be expanded to isolate whether gains derive from the memory mechanism itself or from other implementation choices; current reporting leaves this unclear.

    Authors: We have expanded §4.3 with two new controlled ablations: (1) two-phase retrieval versus single-phase semantic retrieval, isolating the contribution of the noise-filtering stage (~7–9% absolute gain); (2) full MemRL versus a no-update memory baseline, confirming that the runtime RL updates on episodic memory account for the majority of the observed improvement. Results are reported in new tables with the same evaluation protocol. revision: yes

Circularity Check

0 steps flagged

No significant circularity in empirical claims

full rationale

The paper describes an empirical method for runtime agent improvement using episodic memory and environmental feedback, with performance claims grounded in experiments on external benchmarks (HLE, BigCodeBench, ALFWorld, Lifelong Agent Bench). No derivation chain, equations, or theoretical steps are presented that reduce to self-defined inputs, fitted parameters renamed as predictions, or load-bearing self-citations. The approach is framed as non-parametric and benchmark-driven, making the central outperformance claims independently testable rather than circular by construction.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Review performed on abstract only; no explicit free parameters, axioms, or invented entities are stated in the provided text.

pith-pipeline@v0.9.0 · 5498 in / 1056 out tokens · 38535 ms · 2026-05-17T14:44:15.915908+00:00 · methodology

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Reference graph

Works this paper leans on

40 extracted references · 40 canonical work pages · cited by 16 Pith papers · 1 internal anchor

  1. [1]

    Tent: Fully Test-time Adaptation by Entropy Minimization

    URL http://incompleteideas.net/ book/the-book-2nd.html. Tulving, E. et al. Episodic and semantic memory.Organi- zation of memory, 1(381-403):1, 1972. Wang, D., Shelhamer, E., Liu, S., Olshausen, B., and Darrell, T. Tent: Fully test-time adaptation by entropy minimiza- tion.arXiv preprint arXiv:2006.10726, 2020. Wang, G., Xie, Y ., Jiang, Y ., Mandlekar, A...

    work page internal anchor Pith review Pith/arXiv arXiv 1972

  2. [2]

    URL https://openreview.net/forum? id=WE_vluYUL-X. Ye, Y . Task memory engine: Spatial memory for robust multi-step llm agents.arXiv preprint arXiv:2505.19436, 2025. Yu, Q., Zhang, Z., Zhu, R., Yuan, Y ., Zuo, X., Yue, Y ., Dai, W., Fan, T., Liu, G., Liu, L., et al. Dapo: An open-source llm reinforcement learning system at scale.arXiv preprint arXiv:2503.1...

    work page arXiv 2025

  3. [3]

    2.Update Rule.The utility is updated via the linear EMA rule with learning rateα∈(0,1): Qt+1 = (1−α)Q t +αr t

    Stationary Reward.The reward rt at step t is drawn from a distribution induced by the stochastic action generation a∼p LLM(at|st, m), with a constant meanβ(s, m) =E[r t|s, m]and finite varianceσ 2. 2.Update Rule.The utility is updated via the linear EMA rule with learning rateα∈(0,1): Qt+1 = (1−α)Q t +αr t. Derivation of Error Dynamics.Let et ≜Q t −β(s, m...

    work page

  4. [4]

    cold start

    Trust Region:It constrains the policy to the support set S, preventing the agent from retrieving high-Q but semantically irrelevant memories (out-of-distribution errors). 2.Regularization:It stabilizes the learning dynamics during the “cold start” phase when Q-estimates are noisy. A.4.3. OPTIMIZATION VIAGENERALIZEDEXPECTATION-MAXIMIZATION(GEM) We treat th...

    work page arXiv 1998

  5. [10]

    role": ...,

    What to avoid next time Provide a brief reflection: Stored memory content templates. # Successful memory Task: {task_description} SCRIPT: {script} TRAJECTORY: {trajectory} # Failure memory TASK REFLECTION: Task: {task_description} What went wrong: {reflection} Failed approach: {failed_trajectory} 32 MemRL: Self-Evolving Agents via Runtime Reinforcement Le...

    work page

  6. [16]

    status":

    What to avoid next time Provide a brief reflection: Stored memory content templates. 33 MemRL: Self-Evolving Agents via Runtime Reinforcement Learning on Episodic Memory # Successful memory Task: {task_description} SCRIPT: {script} TRAJECTORY: {trajectory} # Failure memory TASK REFLECTION: Task: {task_description} What went wrong: {reflection} Failed appr...

    work page

  7. [20]

    34 MemRL: Self-Evolving Agents via Runtime Reinforcement Learning on Episodic Memory Task: {task_description} Failed trajectory: {failed_trajectory} This task failed

    Focus on the strategy and key decisions, not detailed actions Trajectory: {trajectory} High-level script: Failure reflection prompt. 34 MemRL: Self-Evolving Agents via Runtime Reinforcement Learning on Episodic Memory Task: {task_description} Failed trajectory: {failed_trajectory} This task failed. Analyze what went wrong and suggest improvements for futu...

    work page

  8. [22]

    What to avoid next time Provide a brief reflection: Stored memory content templates. # Successful memory Task: {task_description} SCRIPT: {script} TRAJECTORY: {trajectory} # Failure memory TASK REFLECTION: Task: {task_description} What went wrong: {reflection} Failed approach: {failed_trajectory} LLB (LifelongAgentBench): Experience Summarization Prompts ...

    work page

  9. [23]

    Generic enough to apply to similar tasks

    work page

  10. [24]

    Specific enough to provide useful guidance

    work page

  11. [25]

    3-5 high-level steps maximum

    work page

  12. [26]

    Task: {task_description} Failed trajectory: {failed_trajectory} This task failed

    Focus on the strategy and key decisions, not detailed actions Trajectory: {trajectory} High-level script: Failure reflection prompt. Task: {task_description} Failed trajectory: {failed_trajectory} This task failed. Analyze what went wrong and suggest improvements for future similar tasks. Focus on:

    work page

  13. [27]

    Incorrect assumptions

    work page

  14. [28]

    What to avoid next time Provide a brief reflection: Stored memory content templates. # Successful memory Task: {task_description} SCRIPT: {script} TRAJECTORY: {trajectory} # Failure memory TASK REFLECTION: Task: {task_description} What went wrong: {reflection} Failed approach: {failed_trajectory} 36 MemRL: Self-Evolving Agents via Runtime Reinforcement Le...

    work page

  15. [29]

    [{img_id_1}] ({source_1})

    work page

  16. [30]

    Message ordering

    [{img_id_2}] ({source_2}) ... Message ordering. 37 MemRL: Self-Evolving Agents via Runtime Reinforcement Learning on Episodic Memory

    work page

  17. [31]

    system: exact-match OR multiple-choice format prompt

    work page

  18. [32]

    system: optional reflection note (if enabled)

    work page

  19. [34]

    Thought: your thoughts.\nAction: your next action

    user: question content (text + optional images) ALFWorld: Generation and Inference Prompts Base system prompt (ReAct format + action space). Interact with a household to solve a task. Imagine you are an intelligent agent in a household environment and your target is to perform actions to complete the task goal. At the beginning of your interactions, you w...

    work page

  20. [35]

    take {obj} from {recep}

    work page

  21. [36]

    move {obj} to {recep}

    work page

  22. [37]

    clean {obj} with {recep}

    work page

  23. [38]

    heat {obj} with {recep}

    work page

  24. [39]

    Nothing happened

    cool {obj} with {recep} where {obj} and {recep} correspond to objects and receptacles. After your each turn, the environment will give you immediate feedback based on which you plan your next few steps. if the envrionment output "Nothing happened ", that means the previous action is invalid and you should try more options. Your response should use the fol...

    work page

  25. [40]

    system: base ALFWorld system prompt

    work page

  26. [41]

    user/assistant: selected few-shot example dialogue (sequence of messages)

    work page

  27. [42]

    system: optional retrieved memory context

    work page

  28. [43]

    user: new task prompt

    work page

  29. [44]

    instruct

    loop: append user Observation: ..., model replies with Thought/Action BCB (BigCodeBench): Generation and Inference Prompts Retrieved memory injection (system message). [Retrieved Memory Context] ### Memory 1 (id={mem_id_1}, sim={similarity_1}) {memory_content_1} ### Memory 2 (id={mem_id_2}, sim={similarity_2}) {memory_content_2} ... Dataset-provided task ...

    work page

  30. [45]

    system: optional [Retrieved Memory Context]

    work page

  31. [46]

    39 MemRL: Self-Evolving Agents via Runtime Reinforcement Learning on Episodic Memory You are an execution-focused AI agent solving database and operating-system tasks

    user: {bcb_task_prompt} LLB (LifelongAgentBench): Generation and Inference Prompts Base system prompt. 39 MemRL: Self-Evolving Agents via Runtime Reinforcement Learning on Episodic Memory You are an execution-focused AI agent solving database and operating-system tasks. You may receive a [Retrieved Memory Context] block with past experiences from similar ...

    work page

  32. [47]

    After your reasoning, include exactly ONE action line: - Action: Operation - Action: Answer

    work page

  33. [48]

    Do not add any extra text after that block

    If Action: Operation, put exactly ONE SQL statement in the FIRST fenced code block using ‘‘‘sql, on a single line. Do not add any extra text after that block

    work page

  34. [49]

    Strict output constraint (OS tasks)

    If Action: Answer, include ‘Final Answer: ...‘ on the next line and do not add extra text after that. Strict output constraint (OS tasks). STRICT OUTPUT FORMAT (LLB:OS, do not violate):

    work page

  35. [50]

    After your reasoning, include exactly ONE action line: - Act: bash - Act: finish

    work page

  36. [51]

    Do not include any other code blocks

    If Act: bash, the next lines MUST be a ‘‘‘bash fenced code block with your Bash commands. Do not include any other code blocks

    work page

  37. [52]

    If Act: finish, it must be the last line (no code blocks, no extra text)

    work page

  38. [53]

    Retrieved memory injection block

    Do NOT use ‘Action:‘ in OS tasks (use ‘Act:‘ only). Retrieved memory injection block. [Retrieved Memory Context] === SUCCESSFUL EXPERIENCES (Learn from these) === [SUCCESS 1] [TYPE: {mem_type}] {content} === FAILED EXPERIENCES (Avoid these mistakes) === [FAILURE 1] [TYPE: {mem_type}] {content} Prompt assembly ordering (system prompt). 40 MemRL: Self-Evolv...

    work page

  39. [54]

    optional [Retrieved Memory Context]

    work page

  40. [55]

    strict output format block appended at the end (task-aligned) 41

    work page