Recognition: 1 theorem link
· Lean TheoremMemPO: Self-Memory Policy Optimization for Long-Horizon Agents
Pith reviewed 2026-05-15 18:17 UTC · model grok-4.3
The pith
Agents can learn to manage their own memory by crediting effectiveness, cutting tokens over 67% while raising F1 scores.
A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.
Core claim
The self-memory policy optimization algorithm enables the agent to autonomously summarize and manage memory during interaction by improving the credit assignment mechanism based on memory effectiveness, so the policy model selectively retains crucial information, significantly reducing token consumption while preserving task performance.
What carries the argument
Credit assignment based on memory effectiveness, which trains the policy to decide what information to keep or discard in line with task objectives.
Load-bearing premise
The learned rule for crediting memory will keep every piece of information that will later prove necessary for the task.
What would settle it
A test episode in which the memory policy discards a fact required for correct later actions, causing failure even though the policy scored high effectiveness during training.
read the original abstract
Long-horizon agents face the challenge of growing context size during interaction with environment, which degrades the performance and stability. Existing methods typically introduce the external memory module and look up the relevant information from the stored memory, which prevents the model itself from proactively managing its memory content and aligning with the agent's overarching task objectives. To address these limitations, we propose the self-memory policy optimization algorithm (MemPO), which enables the agent (policy model) to autonomously summarize and manage their memory during interaction with environment. By improving the credit assignment mechanism based on memory effectiveness, the policy model can selectively retain crucial information, significantly reducing token consumption while preserving task performance. Extensive experiments and analyses confirm that MemPO achieves absolute F1 score gains of 25.98% over the base model and 7.1% over the previous SOTA baseline, while reducing token usage by 67.58% and 73.12%. The code is released at https://github.com/TheNewBeeKing/MemPO.
Editorial analysis
A structured set of objections, weighed in public.
Referee Report
Summary. The paper proposes MemPO, a self-memory policy optimization algorithm for long-horizon agents. It enables the policy model to autonomously summarize and manage its own memory during environment interactions by improving credit assignment based on memory effectiveness, allowing selective retention of task-critical information. This is claimed to yield absolute F1 score gains of 25.98% over the base model and 7.1% over prior SOTA while reducing token usage by 67.58% and 73.12%, with code released at the provided GitHub link.
Significance. If the experimental claims hold under scrutiny, the work could meaningfully advance long-horizon agent design by replacing external memory lookup with internalized, task-aligned memory management. The combination of performance gains, efficiency improvements, and released code provides a concrete basis for verification and follow-on research in RL-based agents.
major comments (2)
- [§4] §4 (Experiments): The manuscript reports large F1 and token-usage gains but provides no description of the evaluation protocol, including task domains, number of independent runs, baseline implementations, or statistical significance tests. Without these details the central performance claims cannot be assessed from the text alone.
- [§3.2] §3.2 (Credit Assignment): The memory-effectiveness credit assignment rule is described at the algorithmic level but lacks an explicit mathematical formulation or derivation showing how it differs from standard policy-gradient credit assignment; this makes it impossible to verify whether the reported gains follow from the stated mechanism or from other unablated factors.
minor comments (2)
- [Abstract] Abstract: The phrase 'extensive experiments and analyses' is used without naming the benchmarks or domains, which should be stated explicitly even in the abstract.
- [§3] Notation: The distinction between 'memory content' and 'memory effectiveness' is introduced without a clear notational convention, making some algorithmic steps harder to follow.
Simulated Author's Rebuttal
We appreciate the referee's positive assessment and recommendation for minor revision. We address each major comment below and will update the manuscript accordingly to improve clarity and verifiability.
read point-by-point responses
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Referee: [§4] §4 (Experiments): The manuscript reports large F1 and token-usage gains but provides no description of the evaluation protocol, including task domains, number of independent runs, baseline implementations, or statistical significance tests. Without these details the central performance claims cannot be assessed from the text alone.
Authors: We thank the referee for highlighting this gap. The main text indeed omits a consolidated description of the protocol. In the revised manuscript we will expand Section 4 with a dedicated subsection that specifies the task domains (long-horizon reasoning benchmarks), the number of independent runs (five runs with distinct random seeds), baseline reproduction details, and statistical significance tests (paired t-tests with reported p-values and standard deviations). These additions will make the reported F1 and token reductions directly verifiable from the text. revision: yes
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Referee: [§3.2] §3.2 (Credit Assignment): The memory-effectiveness credit assignment rule is described at the algorithmic level but lacks an explicit mathematical formulation or derivation showing how it differs from standard policy-gradient credit assignment; this makes it impossible to verify whether the reported gains follow from the stated mechanism or from other unablated factors.
Authors: We agree that an explicit derivation is necessary. In the revised Section 3.2 we will insert the mathematical formulation of the memory-effectiveness credit assignment, including the modified advantage estimator that multiplies the standard advantage by a memory-effectiveness factor (computed from downstream task reward contribution of retained summaries). We will also provide the derivation showing how this term augments the policy-gradient objective relative to vanilla REINFORCE or PPO, thereby enabling selective retention of task-critical information. revision: yes
Circularity Check
No significant circularity in derivation chain
full rationale
The paper presents MemPO as a new algorithmic method enabling agents to autonomously summarize and manage memory via an improved credit assignment mechanism. Claims of F1 gains and token reductions are supported by experimental results rather than any closed-form derivation or first-principles equations. No load-bearing steps reduce to self-definition, fitted inputs renamed as predictions, or self-citation chains; the approach is externally validated through code release and benchmarks.
Axiom & Free-Parameter Ledger
Forward citations
Cited by 2 Pith papers
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RoboMemArena: A Comprehensive and Challenging Robotic Memory Benchmark
RoboMemArena is a new large-scale robotic memory benchmark with real-world tasks, and PrediMem is a dual VLA system that outperforms baselines by managing memory buffers with predictive coding.
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Experience Compression Spectrum: Unifying Memory, Skills, and Rules in LLM Agents
The Experience Compression Spectrum unifies memory, skills, and rules in LLM agents along increasing compression levels and identifies the absence of adaptive cross-level compression as the missing diagonal.
discussion (0)
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