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RoMeRL: Balancing Feedback Coverage and the Memory-Reward Trap in Self-Evolving Agent Memory via Reduced-Order Utility States

Yi Yang, Zhennan Chen, Yihong Zhuang, Tiehan Fan, Yinan Chen, Jian Li, Jian Yang, Ying Tai

arXiv:2608.02508v3cs.LGcs.CL

TL;DR

Self-evolving agent memory disperses feedback across growing utility spaces and risks misleading reward updates for irrelevant memories. RoMeRL replaces trajectory-indexed utilities with fixed-dimensional task states, achieving the highest average score across three benchmarks while reducing memory and inference costs.

  • Problem

    Trajectory-indexed utilities grow with interaction history, dispersing feedback and exposing more variables to misattributed rewards, including the memory-reward trap.

  • Method

    RoMeRL replaces growing trajectory-indexed utilities with fixed-dimensional per-task states factorized by outcome polarity and memory dynamics.

  • Results

    0.753 overall average score, 2.9 percentage points above the strongest baseline across ten evaluation units spanning three benchmarks.

  • Takeaways & Limitations

    Across LifelongAgentBench, ALFWorld, and AppWorld, reduced-order utility states support higher task performance with lower memory size, MRT exposure, and inference cost.

  • Takeaways & Limitations

    RoMeRL relies on outcome-level rewards, does not fully resolve causal credit assignment, and requires coordinate-level causal labels to estimate transition quantities.

Abstract

from arXiv · show

Learning-based memory systems for self-evolving LLM agents face two tightly coupled challenges. First, trajectory-indexed utilities grow with the interaction history, thereby dispersing limited feedback over an ever-expanding state space. Second, because trajectory-level rewards are jointly assigned to co-retrieved memories, irrelevant experiences may receive misleading utility updates and consequently enter the memory-reward trap. To address these challenges, we introduce Reduced-Order Memory Reinforcement Learning (RoMeRL), which represents the growing trajectory-indexed utility space using a fixed-dimensional per-task memory state factorized by outcome polarity and memory dynamics. RoMeRL incorporates new experiences through a fixed set of semantic coordinates whose contents are updated or replaced over time, thereby concentrating feedback over a bounded utility support. Theoretically, we show that this reduced-order parameterization increases the average feedback received by each utility coordinate and characterize the steady-state occupancy of erroneous coordinates under a generic coordinate-transition model. Empirically, across ALFWorld and LifelongAgentBench, RoMeRL improves task performance, reduces the Cold-Q ratio by 80.0%, increases feedback density by approximately 6.0 times, reduces the maintained memory size by 84.4%, and cuts LLM calls by 21.1%. These results show that reduced-order utility states support efficient self-evolving agent memory while limiting persistent reward contamination. Code is available at: https://github.com/YOUNG-fnxm/RoMeRL

1 INTRODUCTION

RoMeRL addresses the tension between sparse feedback coverage and reward misattribution in trajectory-indexed agent memory. It replaces the expanding utility space with fixed-dimensional, outcome- and dynamics-factorized states and improves benchmark performance.

  • Problem: Trajectory-indexed utilities expand with experience, diluting limited feedback and exposing more memories to the memory-reward trap.This causes utility cold starts, concentrated updates, and low feedback density; stronger exploration improves coverage but can worsen task performance through reward misattribution.
  • Method: RoMeRL replaces each task’s growing memory utility space with a fixed-dimensional state factorized by outcome polarity and memory dynamics.New experiences are incorporated through fixed semantic coordinates whose contents are updated or replaced over time, concentrating feedback on bounded utility support.
  • Results: 0.753 was RoMeRL’s highest overall average score across the ten reported evaluation units, exceeding the strongest baseline on all three benchmarks by 2.9 percentage points.Evaluation covered LifelongAgentBench, ALFWorld, and AppWorld, spanning operating-system and database interaction, embodied household planning, and multi-application workflows.
  • Analysis: RoMeRL characterizes the balance between feedback coverage and reward contamination in reduced-order utility states.The paper analyzes feedback concentration and the steady-state occupancy of erroneous coordinates under a generic coordinate-transition model.

2 RELATED WORK

Related work progresses from workflow-based agent memory, which organizes storage, reflection, retrieval, and forgetting, toward learnable memories that distill trajectories or optimize memory policies with reinforcement learning.

  • Workflow-Based Agent Memory: Workflow-based agent memory builds on retrieval-augmented inference and uses predefined storage, reflection, and retrieval workflows.Generative Agents and MemoryBank add importance-based retrieval, reflection, and forgetting mechanisms.
  • Workflow-Based Agent Memory: Feedback-oriented systems convert task feedback into verbal reflections or transferable insights.Reflexion and ExpeL represent this approach.
  • Learning-Based Agent Memory: Learning-based agent memory treats memory as a learnable component that distills trajectories into evolving reasoning or procedural memories.ReasoningBank and MemP exemplify this direction.
  • Learning-Based Agent Memory: Reinforcement-learning approaches train policies for memory construction, retrieval, update, and deletion.Memory-R1, Agentic Memory, and AtomMem use reinforcement learning for these memory operations.

3 PRELIMINARIES

Outcome-driven agent memory methods learn utilities for stored trajectories and combine them with semantic relevance during retrieval. Retrieved memories receive utility updates from task-level rewards, but the resulting utility state expands with every newly stored trajectory.

  • Reinforcement Learning in Agentic Memory: Outcome-driven memory methods assign each stored trajectory a learnable utility and use it alongside semantic relevance for retrieval.The memory bank at interaction step t is represented as M_t = {(m_i, Q_i,t)}.
  • Reinforcement Learning in Agentic Memory: After a trajectory receives task-level reward r_t, the utilities of the retrieved memories are updated.The update uses learning rate α and changes external memory utilities rather than the underlying LLM parameters.
  • Reinforcement Learning in Agentic Memory: Every newly stored trajectory adds a utility variable, causing the learnable memory state to grow continuously with interaction history.The state is defined as Q_t = (Q_1,t, . . . , Q_Nt,t) ∈ R^N_t.

4 REDUCED-ORDER MEMORY RL

This section formulates memory reinforcement learning as utility estimation over a growing trajectory-indexed state and identifies the memory-reward trap caused by bundle-level reward attribution. It shows that additional retrieval feedback reduces variance without correcting credit mismatch, while erroneous propagation grows with the exposed memory-state support.

  • Trajectory-Indexed Utility: Trajectory-indexed utilities learn each memory independently, although task rewards depend jointly on the complete retrieved context.This mismatch motivates distinguishing observational utility from a memory’s individual marginal contribution.
  • Utility Estimation: More retrievals increase observation counts and reduce statistical variance, but cannot remove task-level baseline drift or observational attribution bias.Additional feedback estimates the raw-return target more precisely rather than converting it into the memory’s marginal contribution.
  • Memory-Reward Trap: The memory-reward trap occurs when a memory has non-positive marginal utility but a positive observational signal relative to its matched no-memory baseline.Retrieval selection, co-retrieved memories, and context interactions can produce this misleading positive signal.
  • Memory-Reward Trap: Under the trap, repeated Q-based retrieval or retention can reinforce memories with non-positive marginal utility because observational bias makes their update signal positive.The update condition is θ_i ≤ 0 < θ_i + a_i.
  • Exploration Dilemma: The expected support exposed to erroneous reward propagation grows with the memory-state dimension N_t, creating an exploration dilemma between feedback coverage and contamination risk.Broader exploration exposes more utility variables to potentially misattributed rewards.

5 REDUCED-ORDER MEMORY STATE

RoMeRL replaces the growing trajectory-indexed utility space with a fixed factorized memory state, concentrating feedback over bounded semantic coordinates. This reduction increases per-coordinate feedback and limits erroneous-coordinate exposure while retaining replacement dynamics for persistent contamination.

  • Motivation: Trajectory-indexed utilities require increasing feedback because each stored trajectory adds a utility variable, making the estimation state grow with interaction history.Theorem 2 provides a sufficient per-utility sample condition under bounded rewards and independent samples.
  • State design: RoMeRL uses a fixed 2 × 2 state defined by outcome polarity and memory dynamics, with consolidated coordinates preserving selected evidence and adaptive coordinates tracking current states or transitions.The four coordinates arise from successful versus failed evidence and consolidated versus adaptive dynamics; coordinate contents can change while semantic identities remain fixed.
  • State design: Each new trajectory updates or replaces an existing coordinate rather than adding a persistent utility variable, concentrating feedback over a bounded support.Theorem 3 analyzes a general active dimension d, with d = 4 induced by the factorized design.
  • Feedback concentration: doubled? No. Under balanced allocation, each coordinate receives approximately kT/d feedback signals, while factorized learning with dimension 4 receives Nt/4 times the average feedback of trajectory-indexed learning.Theorem 3 states that state reduction concentrates a fixed feedback budget over fewer utilities, rather than redistributing it across a growing set.
  • Erroneous occupancy: The active dimension d bounds exposed erroneous coordinates, while contamination rate γ and correction rate λ govern persistent erroneous occupancy under the replacement model.For the four-coordinate state, the conditional occupancy bound is obtained by setting d = 4.

6 EXPERIMENTS

RoMeRL is evaluated against retrieval-based, agentic, test-time-scaling, and runtime memory-reinforcement baselines across LifelongAgentBench, ALFWorld, and AppWorld using frozen backbones and matched budgets. It achieves the strongest aggregate performance while improving feedback utilization, reducing resource costs, transferring across backbones, and benefiting from complementary memory coordinates.

  • Overall performance: RoMeRL achieves the highest overall average score, 0.753, outperforming MemRL’s 0.724 by 2.9 percentage points.It also attains the best last-epoch SR on both LifelongAgentBench tasks and five of six ALFWorld task types.
  • Feedback utilization and efficiency: 6.0× higher feedback density accompanies an RoMeRL Cold-Q ratio reduction from approximately 28% to 9.0%.Compared with MemRL, RoMeRL reduces average LLM calls by 120K, from 570K.
  • Cross-model memory transfer: All four model–task combinations improve in score and reduce average execution steps when transferring the frozen memory state.The lower step counts also reduce inference cost because each interaction step typically requires another LLM invocation.
  • Coordinate ablation: Removing NCC lowers both Last-Epoch Success Rate and CSR, while NCC occupies 27.14% of OS and 42.78% of DB coordinates.NCC retains failure-derived experiences with high downstream raw-return utility as reusable negative evidence.
  • Coordinate ablation: Removing PAC mainly reduces Last-Epoch Success Rate, despite PAC occupying only 8.21% of OS and 5.05% of DB coordinates.PAC preserves the first successful trajectory after a failure and provides a sparse recovery signal, complementing NCC’s negative evidence.

7 CONCLUSION AND LIMITATIONS · Appendix · A PROOFS FOR ROMERL

RoMeRL addresses memory-reward traps by replacing expanding trajectory-indexed utilities with compact, per-task coordinates, improving performance while reducing memory and inference costs. Its remaining limitations concern outcome-level rewards, causal attribution requirements, and evaluation beyond current task settings.

  • 7 CONCLUSION AND LIMITATIONS: RoMeRL is a reduced-order memory reinforcement learning framework for self-evolving LLM agents.It targets the memory-reward trap caused by outcome-driven Q updates reinforcing co-retrieved memories with little causal contribution.
  • 7 CONCLUSION AND LIMITATIONS: Outcome-driven updates create attribution bias, while full-pool learning requires feedback that grows with the interaction history.Exploration reduces estimation variance but does not remove attribution bias.
  • 7 CONCLUSION AND LIMITATIONS: RoMeRL uses compact per-task coordinates factorized by outcome polarity and memory dynamics.This parameterization concentrates utility representation rather than expanding it over the full memory pool.
  • 7 CONCLUSION AND LIMITATIONS: Across LifelongAgentBench, ALFWorld, and AppWorld, RoMeRL achieves the highest overall average score while reducing memory-reward-trap exposure, memory size, and inference cost.The cited evaluation spans all three named benchmarks and reports improvements across performance and efficiency dimensions.
  • 7 CONCLUSION AND LIMITATIONS: RoMeRL still relies on outcome-level rewards and therefore does not fully resolve causal credit assignment.This limitation leaves co-retrieved memories vulnerable to receiving utility updates that do not reflect their causal contribution.
  • 7 CONCLUSION AND LIMITATIONS: Estimating γ and λ in Proposition 1 requires coordinate-level causal labels from paired counterfactual rollouts or equivalent attribution.The requirement concerns the transition quantities used in the theoretical analysis.
  • 7 CONCLUSION AND LIMITATIONS: Future work may combine causal attribution with finer-grained rewards and evaluate RoMeRL in open-ended, longer-horizon settings.These directions aim to extend attribution and assessment beyond the current outcome-level and benchmark settings.
  • A PROOFS FOR ROMERL: The appendix provides detailed proofs for the theoretical results in Sections 3 and 4, including the bias-variance decomposition of memory utility estimation.It also discusses implications for outcome-driven memory updates.

A.1 PROOF OF THEOREM 1 · A.2 PROOF OF THEOREM 2 · A.3 PROOF OF THEOREM 3

The proofs characterize persistent credit gaps and reward-trap reinforcement, establish sufficient feedback budgets for simultaneous estimation, and show why reduced-order coordinates concentrate feedback and improve estimator variance under balanced allocation.

  • A.1 PROOF OF THEOREM 1: Random visit counts preserve Theorem 1’s identity conditionally when selected rewards retain their stated properties given ni, but adaptive reward-dependent stopping requires separate analysis.The conditional result applies on the event ni > 0 under non-informative sampling.
  • A.1 PROOF OF THEOREM 1: As visit counts grow, empirical estimates become precise around raw-return target µi but retain the task baseline, attribution bias ai, and gap to marginal contribution θi.The variance term vanishes with ni, while the total credit gap remains.
  • A.1 PROOF OF THEOREM 1: A trapped memory can receive positive raw-return reinforcement when θi ≤0 < µi −v0i, making repeated retrieval more likely when retrieval scores increase with Qi.The exponential update has positive conditional drift whenever Qi,t < µi under the stationary reference model.
  • A.1 PROOF OF THEOREM 1: The runtime fixed-step EMA approaches µi in mean but generally retains a nonzero variance floor, so expected drift does not imply vanishing variance.The MRT argument relies on expected drift toward the observational target, whereas Theorems 1 and 3 concern an empirical-mean estimator.
  • A.2 PROOF OF THEOREM 2: Hoeffding’s inequality and a union bound yield a simultaneous estimation guarantee with failure probability no larger than δ, even when estimates across memories are dependent.The proof uses a common per-memory feedback count and then translates it into total and trajectory budgets.
  • A.2 PROOF OF THEOREM 2: Theorem 2’s budgets are sufficient rather than minimax-optimal, and trajectory scheduling accommodates at most k memory-level updates per trajectory up to a final partial trajectory.No minimax lower bound is claimed.
  • A.3 PROOF OF THEOREM 3: For feedback budget FT, average feedback per coordinate scales as kT/D, while balanced allocation yields O(σ2Nt/(kT)) for trajectory-indexed states and O(4σ2/(kT)) for factorized states.The comparison sets D = Nt versus D = 4 under the same realized feedback budget.
  • A.3 PROOF OF THEOREM 3: The feedback-density identity does not require equal coordinate visits, but the variance bound requires approximately balanced allocation; with unsaturated budgets, FT replaces kT.Highly unbalanced feedback preserves density equality without guaranteeing the stated variance bound.

A.4 PROOF OF PROPOSITION 1 · B IMPLEMENTATION DETAILS

The proof bounds steady-state erroneous occupancy and persistence for reduced-order active-state coordinates, while the implementation section documents configurations for reproducibility.

  • A.4 PROOF OF PROPOSITION 1: Linearity of expectation avoids requiring independence among memories when relating erroneous-coordinate indicators to occupancy.The proof invokes this argument both for the full-state quantity and for the stationary active-coordinate indicators.
  • A.4 PROOF OF PROPOSITION 1: An erroneous coordinate remains erroneous with probability at most 1 −λ, while a clean coordinate becomes erroneous with probability at most γ.This transition bound defines the coordinate-level error dynamics used in the proof.
  • A.4 PROOF OF PROPOSITION 1: The stationary erroneous probability is bounded above by γ/(γ + λ).The bound follows by applying the transition recurrence at stationarity and rearranging.
  • A.4 PROOF OF PROPOSITION 1: The active state has strictly smaller steady-state erroneous occupancy whenever dγ/(γ + λ) < Ntρfull.The comparison uses the occupancy expressions in Equations (26) and (28).
  • A.4 PROOF OF PROPOSITION 1: For a four-coordinate active state, setting d = 4 gives the corresponding conditional occupancy bound.The proof specializes the general coordinate bound to an active state with four coordinates.
  • A.4 PROOF OF PROPOSITION 1: The probability that an erroneous coordinate remains erroneous for at least r further transitions is at most (1 −λ)^r, with expected erroneous residence time at most 1/λ.This residence-time result is stated to be consistent with the stationary occupancy result in Proposition 1.
  • B IMPLEMENTATION DETAILS: The implementation details document the exact model versions, hyperparameter settings, and environmental configurations used in the experiments.This documentation is provided to facilitate reproducibility.

B.1 MODEL SPECIFICATIONS … C ADDITIONAL ABLATION AND COMPARATIVE EXPERIMENTS

The paper specifies deterministic model/API settings, adaptive retrieval and benchmark partitions, then evaluates noise robustness and Q-value quality. Additional analysis distinguishes RoMeRL’s consolidated and adaptive memory coordinates by their update, retention, and replacement roles.

  • B.1 MODEL SPECIFICATIONS: LLM reasoning and generation use Table 4 models accessed through official APIs with fixed temperature for reproducibility and deterministic outputs where feasible.Table 4 provides the model and API configurations.
  • B.2 HYPERPARAMETER SETTINGS: The similarity threshold δ uses the top-20% quantile of each dataset’s task-description cosine-similarity distribution to retain relevant historical experiences.The threshold adapts to dataset density for each reported setting.
  • B.3 DATA PARTITIONING: LifelongAgentBench uses a fixed 7:3 random split with seed 42 for OS and DB, while ALFWorld and AppWorld follow official benchmark splits.Transfer learning is evaluated only for LifelongAgentBench.
  • B.4 BENCHMARK DETAILS: The evaluation spans operating-system and database interaction, embodied household decision-making, and compositional workflows across three benchmarks.LifelongAgentBench focuses on DB and OS, ALFWorld covers household manipulation, and AppWorld tests multistep application-API workflows.
  • B.5 CONTROLLED MRT STRESS TEST: 10% of first-round memory entries are replaced with noisy versions that preserve titles but null key actions or reflections, testing reward contamination.The noise preserves retrieval cues while removing actionable content, and is injected only in the first round.
  • B.5 CONTROLLED MRT STRESS TEST: RoMeRL’s replacement mechanism replaces most noisy contents with higher-quality content, whereas MemRL propagates null operations and UCB-driven exploration amplifies noise.The Final Noise Ratio captures both persistence of injected noise and propagation during later interactions.
  • B.6 Q-VALUE STRATIFICATION AND FEEDBACK COVERAGE.: The point-biserial Pearson correlation between Q-values and memory provenance rises from r = 0.493 for MemRL to r = 0.673 for RoMeRL, while success-derived memories span 2.3% to 80.1% across RoMeRL’s Q bins.The reported success-derived proportions compare the lowest Q-value bin (0.0–0.2) with the highest bin (0.9–1.0).
  • B.7 UTILITY BEYOND BINARY SUCCESS REPLAY.: RoMeRL’s 0.9–1.0 Q bin retains approximately 19.9% failure-derived memories, compared with 5.3% for the baseline, indicating composition beyond binary success replay.The high-Q region therefore remains compositionally diverse under RoMeRL.

C.1 TOY VALIDATION OF THE CONSOLIDATED–ADAPTIVE AXIS · C.2 CAPACITY-MATCHED COMPARISON WITH MEMRL

The toy validation shows that combining consolidated and adaptive memory roles balances rapid adaptation with long-term retention under recurrent non-stationarity. A capacity-matched comparison shows RoMeRL outperforming MemRL-4 despite the same four-item-per-task memory limit, especially in feedback utilization.

  • C.1 TOY VALIDATION OF THE CONSOLIDATED–ADAPTIVE AXIS: When Hreturn > Lshift, one homogeneous timescale cannot satisfy both temporary-regime adaptation and recurring-solution retention.This motivates separate short-horizon adaptive and long-horizon consolidated roles.
  • C.1 TOY VALIDATION OF THE CONSOLIDATED–ADAPTIVE AXIS: The two-slot toy study exhaustively compares C + C, C + A, and A + A under equal capacity and shared action-selection and exploration policies.The recurrent non-stationary K-armed bandit uses K = 10, with stable regimes lasting 100 interactions and temporary regimes lasting 25.
  • C.1 TOY VALIDATION OF THE CONSOLIDATED–ADAPTIVE AXIS: 500 random seeds show C + C preserves recurring stable actions but adapts slowly, while configurations containing adaptive coordinates respond faster to temporary regimes.Phase-aligned curves confirm the adaptation advantage of adaptive-coordinate configurations.
  • C.1 TOY VALIDATION OF THE CONSOLIDATED–ADAPTIVE AXIS: A + A increases return-recovery delay by 50%, whereas C + A reduces it by 74%, giving C + A the best joint balance between adaptation and retention.The result motivates the consolidated–adaptive distinction as a minimal two-timescale design.
  • C.1 TOY VALIDATION OF THE CONSOLIDATED–ADAPTIVE AXIS: A + A and C + A reduce cumulative pseudo-regret by 23% and 31%, respectively, and reduce shift-adaptation delay by 44% and 42%.These results compare both mixed-role configurations against C + C.
  • C.2 CAPACITY-MATCHED COMPARISON WITH MEMRL: MemRL-4 restricts MemRL to at most four trajectory-indexed entries per task, matching RoMeRL’s maximum number of active coordinates.It retains MemRL’s original utility representation, retrieval scoring, and outcome-based utility update while using the same evaluation protocol.
  • C.2 CAPACITY-MATCHED COMPARISON WITH MEMRL: On OS, RoMeRL raises last-epoch SR from 0.806 to 0.824 (+1.8 percentage points) and CSR from 0.814 to 0.838 (+2.4 percentage points) versus MemRL-4.Both systems use the same four-item-per-task capacity.
  • C.2 CAPACITY-MATCHED COMPARISON WITH MEMRL: RoMeRL reduces Cold-Q ratio from 27.05% to 9.06% on OS and from 19.60% to 9.73% on DB, corresponding to relative reductions of 66.5% and 50.4%.Cold-Q is the percentage of retrievable memories receiving no utility update.

D COST AND EFFICIENCY ANALYSIS · D.1 TOKEN CONSUMPTION

RoMeRL reduces LLM-call cost through factorized, role-guided memory states that maintain compact active supports and avoid redundant retrievals. It also improves practical efficiency by reducing ineffective memory generation and helping agents complete tasks in fewer interaction steps.

  • D.1 TOKEN CONSUMPTION: RoMeRL consistently requires fewer runtime LLM calls than MemRL across the full learning trajectory.The factorized per-task memory state maintains a compact active support instead of retrieving and updating memories from a continuously growing pool.
  • D.1 TOKEN CONSUMPTION: Compact active supports reduce retrieval of redundant, stale, or weakly relevant memories and make prompts more targeted.This efficiency follows from factorized per-task memory states rather than a continuously growing full memory pool.
  • D.1 TOKEN CONSUMPTION: RoMeRL avoids generating procedural memories for ineffective trajectories that cannot be promoted or replaced in role-specific slots.This role-guided mechanism reduces the number of LLM calls by preventing unproductive memory-generation work.
  • D.1 TOKEN CONSUMPTION: More reliable success, failure-diagnostic, and recovery information helps the agent complete tasks in fewer interaction steps.Fewer interaction steps further reduce total LLM calls.
  • D.1 TOKEN CONSUMPTION: On LAB OS and DB tasks, RoMeRL achieves lower token consumption and fewer LLM calls than MemRL while maintaining stronger task performance.The result links role-guided memory replacement with both improved task performance and practical efficiency.
  • D.1 TOKEN CONSUMPTION: On DB and OS tasks, RoMeRL’s LLM calls per section are lower and steadily decreasing, whereas MemRL remains consistently high.Figure 8 reports call counts by section for the two tasks.

E CASE STUDY: ROLE-BASED MEMORY ANALYSIS · F PROMPT DETAILS

The case studies show how RoMeRL’s semantic coordinates retain efficient successes, failure warnings, transferable diagnostic experience, and post-failure recoveries. Prompt details specify how trajectories are summarized, memories are formatted and injected, and benchmark-specific outputs are constrained.

  • E CASE STUDY: ROLE-BASED MEMORY ANALYSIS: PCC compresses an identical successful trajectory into a concise, idempotent, verifiable template that retains a high Q-value and reproduces the task in two steps.The retrieved memory has semantic similarity 1 and remains effective during the tenth evaluation section.
  • E CASE STUDY: ROLE-BASED MEMORY ANALYSIS: NAC provides recent failure warnings for similar tasks, but unvalidated reflections can receive successful outcomes through bundle-level Q updates shared across co-retrieved coordinates.The case exposes both the utility of failure-derived counterexamples and their vulnerability to reward contamination.
  • E CASE STUDY: ROLE-BASED MEMORY ANALYSIS: NCC transfers diagnostically valuable failure experience across differing entities by preserving shared user/group creation, shell modification, permission, and verification structure.The origin and target tasks differ in names and paths but share the same operational pattern.
  • E CASE STUDY: ROLE-BASED MEMORY ANALYSIS: PAC retains a successful procedure as the first recovery after failure, and the same trajectory may also occupy PCC when it is the most efficient successful representative.This memory is a successful procedure, not a separate failure-reflection record.
  • F PROMPT DETAILS: RoMeRL separates experience summarization prompts from task-time generation and inference prompts for LifelongAgentBench and ALFWorld, while AppWorld uses native interfaces and official evaluators.The implementation provides exact prompt strings and message templates for these benchmarks.
  • F.1 EXPERIENCE SUMMARIZATION PROMPTS: ALFWorld summarization stores full dialogue trajectories and asks for generic, specific high-level scripts of at most 3-5 steps, or brief reflections on failed attempts.Stored templates distinguish successful memories from failure reflections containing what went wrong and the failed approach.
  • F.2 GENERATION AND INFERENCE PROMPTS: Generation prompts inject successful and failed memories as references, require independent task analysis, and enforce benchmark-specific output formats for ALFWorld and LLB.LLB additionally mandates exact SQL or Bash action structures and appends the strict output block after retrieved memory context.
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