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RetroAgent: From Solving to Evolving via Retrospective Dual Intrinsic Feedback

Xiaoying Zhang, Zichen Liu, Yipeng Zhang, Xia Hu, Wenqi Shao · Mar 9, 2026 · Citations: 0

Simulation Env
Open arXiv Find Implementation RSS feed Shortlist (0)

How to use this page

Low trust

Use this as background context only. Do not make protocol decisions from this page alone.

Best use

Background context only

What to verify

Validate the evaluation procedure and quality controls in the full paper before operational use.

Evidence quality

Low

Derived from extracted protocol signals and abstract evidence.

Abstract

Standard reinforcement learning (RL) for large language model (LLM) agents typically optimizes extrinsic rewards, prioritizing isolated task completion over continual adaptation. Consequently, agents often converge to suboptimal policies due to limited exploration. Furthermore, accumulated experience remains implicitly trapped within model parameters, hindering its explicit reuse for guiding future decisions. Inspired by human retrospective self-improvement, we introduce RetroAgent, an online RL framework that trains agents to master complex interactive environments not only by solving tasks, but by evolving under the joint guidance of extrinsic task rewards and retrospective dual intrinsic feedback. Specifically, RetroAgent employs a hindsight self-reflection mechanism that generates two complementary signals: (1) intrinsic numerical feedback, which rewards promising exploration by tracking real-time incremental subtask progress relative to prior attempts; and (2) intrinsic language feedback, which enables explicit experience reuse by distilling reusable lessons into a memory buffer for subsequent decision-making. To effectively leverage these textual experiences, we propose Similarity & Utility-Aware Upper Confidence Bound (SimUtil-UCB), a retrieval strategy that balances relevance, historical utility, and exploration. Extensive experiments across four challenging agentic tasks show that RetroAgent achieves new state-of-the-art (SOTA) performance. Notably, it surpasses Group Relative Policy Optimization (GRPO) baselines by +18.3% on ALFWorld, +15.4% on WebShop, +27.1% on Sokoban, and +8.9% on MineSweeper, while exhibiting strong test-time adaptation and out-of-distribution generalization.

Abstract-only analysis — low confidence

All signals on this page are inferred from the abstract only and may be inaccurate. Do not use this page as a primary protocol reference.

  • This paper looks adjacent to evaluation work, but not like a strong protocol reference.
  • The available metadata is too thin to trust this as a primary source.
Human Eval Hub LLM-as-Judge Hub Pairwise Preference Hub

Should You Rely On This Paper?

This paper is adjacent to HFEPX scope and is best used for background context, not as a primary protocol reference.

Best use

Background context only

Use if you need

A benchmark-and-metrics comparison anchor.

Main weakness

This paper looks adjacent to evaluation work, but not like a strong protocol reference.

Trust level

Low

Usefulness score

7/100 • Low

Treat as adjacent context, not a core eval-method reference.

Human Feedback Signal

Not explicit in abstract metadata

Evaluation Signal

Detected

Usefulness for eval research

Adjacent candidate

Extraction confidence 45%

If you are doing eval pipeline work, start here:

Human Eval Hub LLM-as-Judge Hub Pairwise Preference Hub Tool-Use Eval Hub

What We Could Verify

These are the protocol signals we could actually recover from the available paper metadata. Use them to decide whether this paper is worth deeper reading.

Human Feedback Types

missing

None explicit

No explicit feedback protocol extracted.

"Standard reinforcement learning (RL) for large language model (LLM) agents typically optimizes extrinsic rewards, prioritizing isolated task completion over continual adaptation."

Evaluation Modes

partial

Simulation Env

Includes extracted eval setup.

"Standard reinforcement learning (RL) for large language model (LLM) agents typically optimizes extrinsic rewards, prioritizing isolated task completion over continual adaptation."

Quality Controls

missing

Not reported

No explicit QC controls found.

"Standard reinforcement learning (RL) for large language model (LLM) agents typically optimizes extrinsic rewards, prioritizing isolated task completion over continual adaptation."

Benchmarks / Datasets

partial

ALFWorld, WebShop

Useful for quick benchmark comparison.

"Notably, it surpasses Group Relative Policy Optimization (GRPO) baselines by +18.3% on ALFWorld, +15.4% on WebShop, +27.1% on Sokoban, and +8.9% on MineSweeper, while exhibiting strong test-time adaptation and out-of-distribution generalization."

Reported Metrics

partial

Relevance

Useful for evaluation criteria comparison.

"To effectively leverage these textual experiences, we propose Similarity & Utility-Aware Upper Confidence Bound (SimUtil-UCB), a retrieval strategy that balances relevance, historical utility, and exploration."

Human Feedback Details

  • Uses human feedback: No
  • Feedback types: None
  • Rater population: Not reported
  • Expertise required: General

Evaluation Details

  • Evaluation modes: Simulation Env
  • Agentic eval: None
  • Quality controls: Not reported
  • Evidence quality: Low
  • Use this page as: Background context only

Protocol And Measurement Signals

Benchmarks / Datasets

ALFWorldWebShop

Reported Metrics

relevance

Research Brief

Metadata summary

Standard reinforcement learning (RL) for large language model (LLM) agents typically optimizes extrinsic rewards, prioritizing isolated task completion over continual adaptation.

Based on abstract + metadata only. Check the source paper before making high-confidence protocol decisions.

Key Takeaways

  • Standard reinforcement learning (RL) for large language model (LLM) agents typically optimizes extrinsic rewards, prioritizing isolated task completion over continual adaptation.
  • Consequently, agents often converge to suboptimal policies due to limited exploration.
  • Furthermore, accumulated experience remains implicitly trapped within model parameters, hindering its explicit reuse for guiding future decisions.

Researcher Actions

  • Compare this paper against nearby papers in the same arXiv category before using it for protocol decisions.
  • Check the full text for explicit evaluation design choices (raters, protocol, and metrics).
  • Use related-paper links to find stronger protocol-specific references.

Caveats

  • Generated from abstract + metadata only; no PDF parsing.
  • Signals below are heuristic and may miss details reported outside the abstract.

Recommended Queries

Research Summary

Contribution Summary

  • Standard reinforcement learning (RL) for large language model (LLM) agents typically optimizes extrinsic rewards, prioritizing isolated task completion over continual adaptation.
  • Inspired by human retrospective self-improvement, we introduce RetroAgent, an online RL framework that trains agents to master complex interactive environments not only by solving tasks, but by evolving under the joint guidance of extrinsic…
  • To effectively leverage these textual experiences, we propose Similarity & Utility-Aware Upper Confidence Bound (SimUtil-UCB), a retrieval strategy that balances relevance, historical utility, and exploration.

Why It Matters For Eval

  • Standard reinforcement learning (RL) for large language model (LLM) agents typically optimizes extrinsic rewards, prioritizing isolated task completion over continual adaptation.
  • Inspired by human retrospective self-improvement, we introduce RetroAgent, an online RL framework that trains agents to master complex interactive environments not only by solving tasks, but by evolving under the joint guidance of extrinsic…

Researcher Checklist

  • Gap: Human feedback protocol is explicit

    No explicit human feedback protocol detected.

  • Pass: Evaluation mode is explicit

    Detected: Simulation Env

  • Gap: Quality control reporting appears

    No calibration/adjudication/IAA control explicitly detected.

  • Pass: Benchmark or dataset anchors are present

    Detected: ALFWorld, WebShop

  • Pass: Metric reporting is present

    Detected: relevance

Related Papers

Papers are ranked by protocol overlap, extraction signal alignment, and semantic proximity.

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