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Travel Back To Move Forward: Retrospective Exploration and Evolving for Long Horizon Agents

Ziang Ye, Wentao Shi, Lei Chen, Zhengzhou Cai, Yu Wang, Zhiyuan Yao, Zihan Wang, Xierui Song, Fuli Feng, Yixin Cao

arXiv:2610.13058v1cs.AIcs.CL

TL;DR

Long-horizon agents can make early mistakes that cascade, while recovery across episodes can require repeating earlier progress. ReTRAIL rewinds within an ongoing task while retaining progress and distilled experience; across four benchmarks, it outperforms methods without rewind and baselines with the same interface.

  • Problem

    Long-horizon agents may make early mistakes that alter the environment and trigger cascading failures, while existing recovery approaches leave a challenge in using abandoned-branch experience within an ongoing task.

    Source: §1 · pp. 1–2

  • Method

    ReTRAIL rewinds to earlier environment states while preserving the execution prefix and using distilled experience, with shared-policy signals for execution and reflection.

    Source: §1 · p. 2, §3 · p. 4, §4.2 · p. 6

  • Results

    Across four interactive benchmarks, ReTRAIL consistently outperforms methods without rewind and baselines with the same interface, with ablations supporting the roles of recovery, retained experience, and progress-based feedback.

    Source: §7 · p. 10

  • Takeaways & Limitations

    The findings support retrospective exploration as a promising foundation for autonomous agents.

    Source: §7 · p. 10

    Evaluation covers four controlled benchmarks, and restoring their states does not reverse real-world consequences of actions.

    Source: ETHICS STATEMENT · p. 10

Abstract

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Large language model agents increasingly operate in long-horizon environments where early mistakes can trigger cascading failures and trap agents in dead-end trajectories. Agents must therefore be able to proactively recover from such states and turn their exploratory experience into guidance for subsequent decisions. We introduce ReTRAIL, a framework that enables agents to rewind the environment to earlier states while preserving valid progress and carrying forward knowledge learned from abandoned exploration. The environment state moves backward while the agent's knowledge continues to move forward. ReTRAIL trains a shared policy with two complementary signals: State-Anchored GRPO compares alternative continuations from revisited states to improve execution, while a branch-advancement reward guides reflection through the progress of subsequent exploration. Experiments across four interactive benchmark MineSweeper, Sokoban, ALFWorld and ScienceWorld show that simply adding a rewind primitive provides limited benefit and can even degrade performance. In contrast, ReTRAIL enables agents to rewind from unproductive states and use insights from abandoned branches to guide subsequent exploration. These results demonstrate the value of coupling environment rewinding with experience reuse within an ongoing task for long-horizon task solving. The code is available at https://github.com/whi497/ReTRAIL.

1 INTRODUCTION

ReTRAIL addresses recovery from unproductive branches by rewinding the environment while retaining valid progress and experience. Its evaluations find benefits from proactive rewinding and reflection-guided retries across four benchmarks, unlike rewind alone, which can degrade performance.

1 INTRODUCTION

  • Long-horizon agents can make early mistakes that alter the environment, trigger cascading failures, and accumulate irrelevant information in context.Recovery requires revisiting viable environment states without discarding valid progress or useful experience.Source: §1 · p. 1
  • Prior approaches revise plans locally, adapt across episodes only after termination, or revisit decisions through backtracking, leaving proactive recovery within an ongoing task a central challenge.Cross-episode adaptation restarts from the initial state, requiring earlier progress to be repeated.Source: §1 · pp. 1–2
  • ReTRAIL proactively abandons an unproductive branch, rewinds to an earlier state, and resumes with its retained execution prefix and distilled experience.State-Anchored GRPO compares continuations from the same state; a branch-advancement reward trains reflection from subsequent progress.Source: §1 · p. 2
  • Across four benchmarks, ReTRAIL benefits from proactive rewinding and further gains from reflection-guided retries, while simply adding rewind can degrade performance.On MineSweeper, success reaches 95.3% with up to three reflection-guided attempts, versus 70.8% for the strongest baseline under the same protocol.Source: §1 · p. 2
  • The paper evaluates ReTRAIL on MineSweeper, Sokoban, ALFWorld, and ScienceWorld, reporting larger gains on longer and harder tasks.It attributes the value of the approach to retaining experience that can guide subsequent exploration.Source: §1 · p. 2

2 RELATED WORK

Related work addresses execution recovery and adaptation from experience, but ReTRAIL couples environment-level rewind with retained progress and learning within an ongoing task.

2.1 LONG-HORIZON EXECUTION AND RECOVERY

  • Context-only revision does not reverse environment changes, while existing rollback approaches use prompted or supervised mechanisms to restore earlier states and carry feedback forward.ReTRAIL instead trains a shared execution and reflection policy with reinforcement learning.Source: §2.1 · pp. 2–3
  • Multi-episode adaptation carries experience into a new attempt after restarting from the initial state, whereas ReTRAIL abandons a branch and rewinds while retaining the execution prefix and experience.The figure contrasts a restart after an unsuccessful attempt with recovery before terminal failure.Source: Figure 1 · p. 3

2.2 TEST-TIME ADAPTATION AND SELF-EVOLVING AGENTS

3 RETROSPECTIVE EXPLORATION

Retrospective exploration lets an agent recover within an ongoing attempt by rewinding to an earlier state while preserving its valid prefix and carrying forward distilled experience.

3 RETROSPECTIVE EXPLORATION

  • Unlike multi-episode adaptation, which restarts from the initial state and requires rebuilding environment progress, retrospective exploration restores a previously visited state during an ongoing attempt.The agent preserves the execution prefix and uses experience from the abandoned branch to guide another continuation.Source: §3 · p. 4
  • A forward segment runs from the initial or restored state to a recovery boundary or trial termination, and the agent may proactively rewind when a branch is unlikely to make progress.Recovery may also be triggered by a failure signal; selecting an earlier state divides the path into a retained prefix and abandoned suffix.Source: §3 · p. 4
  • Before discarding the abandoned branch, the agent distills useful information into a compact reflection about constraints, failure causes, actions to avoid, or alternative directions.Source: §3 · p. 4
  • After restoration, execution resumes from the selected earlier state with the retained prefix and updated experience, so subsequent decisions use a better-informed context.A trial comprises forward segments; later segments resume from selected earlier states, and the discarded suffix may span parts of multiple earlier segments.Source: §3 · p. 4

4 LEARNING FROM RETROSPECTIVE EXPLORATION

ReTRAIL trains execution and reflection together using state-anchored continuation comparisons and progress feedback from resumed branches. The signals assign execution credit among alternatives and assess the utility of experience distilled at recovery.

4 LEARNING FROM RETROSPECTIVE EXPLORATION

  • A shared execution and reflection policy uses two signals: compare continuations from the same state and prefix, then compare progress after recovery with progress on the abandoned branch.The figure depicts separate masked objectives updating the shared policy.Source: §4 · p. 5, Figure 2 · p. 5

4.1 STATE-ANCHORED GRPO OVER REVISITED STATES

  • State-Anchored GRPO groups alternative continuations by task, starting environment state, and retained history, including suffixes of earlier segments and new post-rewind continuations.Samples keep their original generation history and experience, which may differ within a group.Source: §4.1 · pp. 5–6
  • Execution advantage combines task-wide endpoint progress with state-relative return comparisons, favoring higher-return continuations from the same state and prefix.The state-relative term is zero for singleton groups, while execution credit applies to continuation tokens, including a model-initiated rewind decision.Source: §4.1 · p. 6

4.2 LEARNING REFLECTION FROM BRANCH ADVANCEMENT

  • Because reflection's value is realized through the resumed continuation, the method compares that continuation's progress with the abandoned suffix from the same restored state.The abandoned suffix may span portions of multiple segments and need not match a single continuation used for execution grouping.Source: §4.2 · p. 6
  • An environment-verifiable progress measure supplies a branch-advancement reward: greater progress after recovery indicates greater utility of the retained experience.The two objectives update execution tokens, including rewind, and reflection tokens, with group-normalized branch-advancement rewards supervising reflection.Source: §4.2 · p. 6

5 EXPERIMENTS

ReTRAIL is evaluated on four interactive benchmarks under forward-only, rewind-enabled, and reflection-guided retry settings. It leads recovery-enabled success while retaining competitive forward execution.

5.1 EXPERIMENTAL SETUP

  • P@1 tests forward-only execution, T@1 permits proactive rewind, and T@3 allows up to three reflection-guided macro-attempts; each setting uses a 60-call budget.The four text-interaction benchmarks span deduction, irreversible planning, instruction following, and procedural reasoning.Source: §5.1 · p. 7, Table 1 · p. 7

5.2 MAIN RESULTS

  • ReTRAIL leads T@1 and T@3 on all four benchmarks, beating the strongest T@3 baseline by 24.5 points on MineSweeper and 10.2 on ScienceWorld.Source: Table 1 · p. 7, §5.2 · p. 8
  • Within-attempt proactive recovery gains 1.3 points on ALFWorld and 10.9 on Sokoban; MineSweeper success rises from 63.5 at T@1 to 95.3 at T@3.The MineSweeper retry gain is 31.8 points, compared with 9.1 for GiGPO and 13.6 for LaMer.Source: §5.2 · p. 8
  • Against LaMer, median states rise 11.0→14.0 on MineSweeper and 8.0→11.5 on Sokoban, while successful-trial actions fall 53.0→40.0 and 48.5→37.0.Source: §5.2 · p. 8, Figure 3 · p. 8

5.3 SCALING INSTANCE DIFFICULTY AND RECOVERY ATTEMPTS

  • At the hardest setting, success reaches 63.9 versus 45.3 on MineSweeper and 55.2 versus 45.5 on Sokoban, for ReTRAIL versus the strongest baseline.Source: §5.3 · p. 8, Figure 4 · p. 9
  • MineSweeper success rises from 63.5 at T@1 to 97.8 at T@4; Sokoban reaches 95.1 at T@4, with ReTRAIL highest at each attempt budget.The MineSweeper gain is 34.3 points.Source: §5.3 · pp. 8–9, Figure 4 · p. 9

6 ANALYSIS

The analysis finds that ReTRAIL’s recovery gains increase with task horizon and that its rewind, reflection, and execution-learning components each contribute to performance. The ablations distinguish the benefits of proactive recovery and distilled branch experience from those of the proposed learning objectives.

Ablation

  • Removing model-initiated rewind lowers T@1 from 63.5 to 58.1 on MineSweeper and 87.5 to 72.3 on Sokoban; raw abandoned suffix lowers T@3 to 69.7 and 87.2, respectively.The passage attributes the latter change to distilled branch experience providing more useful guidance than raw history alone.Source: §6 · p. 9
  • Replacing branch-advancement reward with post-recovery progress alone lowers T@3 from 95.3 to 80.2 on MineSweeper and 93.7 to 84.9 on Sokoban.The results support using subsequent branch progress to guide reflection.Source: §6 · p. 9
  • Replacing State-Anchored GRPO with GiGPO lowers MineSweeper T@1/T@3 from 63.5/95.3 to 57.6/90.1 and Sokoban T@1/T@3 from 87.5/93.7 to 84.2/90.3.This comparison evaluates the credit-assignment designs within the same retrospective-exploration framework.Source: §6 · p. 9

Recovery Gains across Task Horizons

  • Reflection-guided retry gain rises from 4.5 to 19.0 percentage points from short to long tasks, while proactive gain rises from 1.3 to 7.8 points.ReTRAIL surpasses LaMer by 5.8 points on long tasks, although LaMer has a slightly larger retry gain on short tasks.Source: §6 · p. 9

7 CONCLUSION

ReTRAIL combines environment-level rewind with retained experience from abandoned branches, training a shared execution and reflection policy. Across four interactive benchmarks, it outperforms methods without rewind and baselines using the same rewind interface.

7 CONCLUSION

  • ReTRAIL combines execution recovery with experiential learning through retrospective exploration, retaining abandoned-branch experience while agents revisit earlier states.Its shared execution and reflection policy uses state-anchored comparisons to improve execution and branch-advancement feedback to guide reflection.Source: §7 · pp. 9–10
  • Across four interactive benchmarks, ReTRAIL consistently outperforms methods without rewind and baselines equipped with the same interface.Ablations support complementary roles for recovery, retained experience, and progress-based feedback.Source: §7 · p. 10
  • The findings identify retrospective exploration as a promising foundation for agents to recover from setbacks, adapt through accumulated experience, and sustain progress toward long-horizon goals.Source: §7 · pp. 9–10

AI USE STATEMENT

The authors report using generative AI for language polishing and literature assistance, while retaining responsibility for the final manuscript and its claims.

AI USE STATEMENT

  • Generative AI assistance improved grammar, sentence flow, clarity, and readability while preserving intended technical meaning.Source: AI USE STATEMENT · p. 10
  • Literature assistance supported identifying relevant prior work and organizing and summarizing existing research.Source: AI USE STATEMENT · p. 10
  • The authors state that they reviewed AI-assisted material and remain responsible for the final content, claims, and artifacts.Source: AI USE STATEMENT · p. 10

ETHICS STATEMENT

The work evaluates recovery and learning in four controlled benchmark environments. The authors distinguish benchmark state restoration from reversing real-world consequences and call for safeguards around consequential external actions.

ETHICS STATEMENT

  • The evaluation covers MineSweeper, Sokoban, ALFWorld, and ScienceWorld as controlled benchmark environments.Source: ETHICS STATEMENT · p. 10
  • Benchmark state restoration should not be interpreted as reversing real-world consequences; deployment beyond these benchmarks calls for authorization, safeguards, and oversight for consequential or irreversible actions.Source: ETHICS STATEMENT · p. 10
  • The authors call for privacy and access controls for retained interaction histories and reflection memories, and compliance with applicable licenses and terms for data, models, and software.Source: ETHICS STATEMENT · p. 10

REPRODUCIBILITY STATEMENT

ReTRAIL’s recovery gains are larger on longer-reference tasks, while the paper notes restoration requirements and unresolved recovery-credit and efficiency issues.

REPRODUCIBILITY STATEMENT

  • Reproducibility materials specify the framework, objectives, benchmarks, partitions, baselines, hyperparameters, evaluation protocols, aggregation, and environment prompts.Appendix D also details restoration, recovery, context management, rollout collection, and policy updates.Source: REPRODUCIBILITY STATEMENT · p. 10

A LIMITATIONS AND FUTURE WORK

  • The current framework assumes state restoration through snapshots or reset-and-replay; the latter requires deterministic transitions and resettable external state.Approximate restoration and irreversible side effects remain extension targets.Source: A LIMITATIONS AND FUTURE WORK · p. 20
  • Execution return ends at rewind boundaries, while subsequent branch advancement supervises reflection; direct feedback on rewind timing and target selection remains a research direction.The paper also identifies restoration and reflection costs and sparse or noisy feedback as efficiency challenges.Source: A LIMITATIONS AND FUTURE WORK · p. 20

B.1 RECOVERY GAINS BY REFERENCE-SOLUTION LENGTH

  • ReTRAIL’s retry gain rises from +4.5 on short tasks to +9.2 on medium and +19.0 on long tasks; LaMer leads the short group at +5.6.ReTRAIL’s retry-gain advantage over LaMer is −1.1, +3.2, and +5.8 points across the groups; its proactive gain rises from +1.3 to +5.3 and +7.8. These descriptive results do not isolate length from other task properties or establish reflection’s causal effect.Source: §B.1 · p. 20

B.2 AVERAGE PERFORMANCE ACROSS BENCHMARKS

This section describes equal-benchmark aggregation and recovery-gain measures, alongside benchmark partitions and environment-specific training signals.

B.2 AVERAGE PERFORMANCE ACROSS BENCHMARKS

  • Table 4 reports equal-weight averages computed from rounded benchmark means, with spread across three evaluation seeds; forward-only results are competitive, not significantly superior on every benchmark.The aggregate point estimates are not additional runs, and minor rounding differences are possible.Source: §B.2 · p. 20, Table 4 · p. 21
  • Figure 5’s upper row reports retry gain and its lower row proactive gain, averaged equally across four environments in percentage points for Short, Medium, and Long groups.The length groups use environment-specific rules.Source: Figure 5 · p. 21

C.1 BENCHMARKS AND DATA PARTITIONS

  • All four benchmarks use text interaction and an environment-restoration interface; Sokoban’s registered variants use 6×6 or 7×7 rooms with two or three boxes.Those Sokoban variants each have 256 training and 128 test entries; counts denote task entries, not repeated rollouts or evaluation seeds.Source: §C.1 · p. 21

C.2 REWARDS AND PROGRESS SIGNALS

  • Training separates per-decision reward (δt), task-wide endpoint score (S), and branch score (V) for reflection, with score definitions varying by environment.Sokoban’s branch score uses evaluator-only breadth-first-search distances; ALFWorld measures satisfied goal conditions and ScienceWorld uses its native task-completion score.Source: §C.2 · pp. 21–22, Table 6 · p. 22

C.3 TRAINING CONFIGURATION AND SAMPLING BUDGET

This appendix section details ReTRAIL’s training and evaluation configuration, including budgets, recovery and data-handling procedures, task-local objectives, and benchmark aggregation. It also describes benchmark prompts for checkpoint-based rewinds and retained branch knowledge, along with validation case studies and their distinction from final evaluation.

C.3 TRAINING CONFIGURATION AND SAMPLING BUDGET

  • Baselines sample 24 trajectories per task and update, while ReTRAIL samples eight trials; both allow 60 calls per trajectory or trial, but nominal call ceilings are not realized compute.This aligns nominal attempt slots: the respective ceilings are 1,440 calls for baselines and 480 for ReTRAIL. All trained methods start from Qwen3-4B and share optimizer, schedule, task splits, and decoding settings.Source: §C.3 · pp. 22–23

C.4 EVALUATION PROTOCOL AND CALL ACCOUNTING

  • P@1 disables recovery, T@1 permits proactive recovery within one macro-attempt, and T@3 also permits failure-triggered recovery up to three attempts.Trials have a global ceiling of 60 model calls, and rewinds do not refund calls; attempt and rewind allowances, remaining calls, and backend branch allowances govern recovery.Source: §C.4 · pp. 23–24

C.5 AGGREGATION ACROSS BENCHMARKS AND SEEDS

  • Reported aggregate means weight the four benchmarks and three evaluation seeds equally, regardless of benchmark instance counts, and include seed standard deviations.Individual seed values cannot be recovered from the reported means.Source: §C.5 · p. 24

D.1 ENVIRONMENT RESTORATION AND CHECKPOINTS

  • Rewind restores snapshots in MineSweeper and Sokoban, while ALFWorld and ScienceWorld reset and replay actions; the chronological archive preserves abandoned-branch generations and contexts.Replay requires deterministic transitions and resettable external state. A checkpoint identifies a path position, and restoration truncates the active path without truncating the archive.Source: §D.1 · p. 24

D.2 RECOVERY TRIGGERS AND REFLECTION

  • Model-initiated and failure-triggered recovery both generate reflection before restoration, while the resumed conversation is assembled differently across environment backends.MineSweeper and Sokoban restore a saved conversation prefix; ScienceWorld includes memories in a subsequent observation, while ALFWorld rebuilds the conversation. The Minesweeper prompt says branch knowledge persists through rewind and distinguishes a global budget from a checkpoint-rolled-back task-solve budget.Source: §D.2 · p. 24, §D.3 · p. 25, §E.1 · p. 29

D.4 ROLLOUT ALGORITHM

  • The rollout algorithm records generations with original contexts in a chronological archive and groups execution decisions into segments between recovery boundaries.Each execution decision is a training sample scored using its remaining segment; invalid outputs consume a call, and restoring a checkpoint neither truncates the archive nor refunds calls.Source: §D.4 · p. 25, §D.5 · p. 25

D.5.2 EXECUTION ADVANTAGES

  • Execution advantages combine segment-level progress with local comparisons among visits sharing a task, state, and retained history.Returns stop at rewind, which has zero transition reward; reflection calls do not enter the return, and singleton or constant-score groups have zero advantage.Source: §D.5.2 · pp. 26–27

D.5.3 REFLECTION REWARDS AND ELIGIBILITY

  • Reflection is rewarded for resumed-branch advancement over the abandoned suffix, and only eligible reflection outputs contribute to the task-local objective.Execution and reflection use separate output masks; the clipped policy surrogate uses original contexts and task-local normalization groups. Normalization can make positive advancement negative relative to other reflections, while a singleton group has zero advantage.Source: §D.5.3 · p. 27, §D.5.4 · p. 27, §D.6 · p. 28

E.1 MINESWEEPER

  • MineSweeper reflection preserves only useful branch knowledge, including mistakes, clue constraints, and a concrete improved plan from the checkpoint.The environment returns to the selected checkpoint while the agent records what led to the branch outcome and what to avoid or try next.Source: §E.1 · p. 30

E.2 SOKOBAN

  • Sokoban separates a global action budget, never refunded by rewinding, from a task-solve budget restored to its checkpoint value, while retaining branch memory.Reflection identifies bad pushes, dead zones, blocked moves, and spatial facts to form a different plan from the restored checkpoint.Source: §E.2 · pp. 31–32

E.3 ALFWORLD

  • ALFWorld rewind restores location, inventory, object locations, and object states, while learned branch memory persists to inform future actions.The global action budget remains spent, whereas rewinding starts a new segment with a fresh current-branch allowance; planning should use learned object locations, prerequisites, and failed-action outcomes.Source: §E.3 · pp. 33–34
  • The ALFWorld reflection prompt asks agents to record failed locations or object choices, missed steps, and useful observations, then end with a rewind action.The memory is intended to help the next attempt avoid the failed branch.Source: §E.3 · p. 35

E.4 SCIENCEWORLD

  • ScienceWorld reflections retain a concise branch memory of failures, useful observations, and an improved plan, while rewind restores an earlier checkpoint without refunding the global action budget.The task-solve budget rolls back to the amount consumed at the restored checkpoint; rewind is suggested for dead ends, inconsistent observations, repeated ineffective actions, or restarting from a better state.Source: §E.4 · pp. 35–36

F CASE STUDIES

  • The case studies illustrate failure-triggered MineSweeper recovery and proactive Sokoban recovery using selected archived validation rollouts, not runs behind the final evaluation tables.Their prompts allow 60 global action turns and 20 active-path turns, whereas the main protocol counts execution and reflection together within 60 model calls; the appendix reports action turns, primitive actions, and reflection calls separately.Source: F CASE STUDIES · p. 38

F.1 MINESWEEPER: RECOVERING AFTER A FAILED REVEAL

These cases show the policy recovering from a failed MineSweeper reveal and leaving a Sokoban loop while retaining earlier progress; neither example isolates reflection’s effect.

F.1 MINESWEEPER: RECOVERING AFTER A FAILED REVEAL

  • At C3, 32 safe cells were open before the policy hit a mine after treating (6,2) as the only remaining cell.The other unopened cell, (5,2), was the safe reveal needed to complete the board.Source: §F.1 · pp. 38–39
  • Five reveals and one reflection call complete the MineSweeper board after rewind, without repeating a reveal.The board returns to C3 with the three preceding safe reveals preserved, while the policy retains the mine location.Source: Figure 6 · p. 38, §F.1 · p. 39

F.2 SOKOBAN: LEAVING A LOOP WHILE PRESERVING A SUBGOAL

  • In Sokoban, rewinding from C10 to C4 preserves the first box on target and enables a plan to finish the second, but a single-episode rollout succeeds in three action turns.This case shows no efficiency advantage, and the logs do not isolate reflection’s effect from state restoration.Source: Figure 7 · p. 39, §F.2 · p. 40

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