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HFEPX · Eval paper review

ReflectPO: Reflection-Guided Policy Optimization for Long-Horizon LLM Agents

Rumei Li, Xunliang Cai

Published

Oct 22, 2026

Citations

0

Trust level

Low

Usefulness score

0/100 (Low)

Extraction confidence

15% (Low)

Derived from extracted protocol signals and abstract evidence.

Rater population

Not reported

Signals refreshed

Oct 22, 2026

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Tags

Long Horizon Tool Use

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.

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

Best use

Background context only

Use if you need

A secondary eval reference to pair with stronger protocol papers.

What to verify

Read the full paper before copying any benchmark, metric, or protocol choices.

Main weakness

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

Human feedback signal
Not explicit
Not explicit in abstract metadata
Evaluation signal
Detected
Eval setup described
Usefulness for eval research
0/100
Adjacent candidate

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

If you are doing eval pipeline work, start here

Human Eval Papers LLM-as-Judge Papers Pairwise Preference Papers Tool-Use Evaluation Search

Abstract

With external tools, large language models (LLMs) can tackle complex tasks through sustained environmental interaction. LLM-based agents are therefore increasingly used to tackle complex long-horizon tasks in real-world environments. However, these tasks involve sequential, interdependent decisions and changes in environmental states. Early execution errors affect subsequent interaction histories and environmental states, and their effects accumulate along the decision chain. To address this challenge, we propose Reflection-Guided Policy Optimization (ReflectPO), a reinforcement learning framework for autonomous error recovery in long-horizon tasks. ReflectPO comprises two core components. The first is Reflection-Guided Sampling. We use branch differences and repeated visits in interaction trajectories to construct hints for error recovery. The second is Tree-Structured Credit Assignment. We organize sampled trajectories into trees and assign action-level credit using parent--child reward differences and recovery gains. Experiments over two long-horizon tool-use benchmarks show that ReflectPO outperforms multiple existing baselines in most domains. It also achieves the best overall performance on average. Further analyses show that ReflectPO effectively improves agents' ability to recognize their own deviations, recover from erroneous states, and continue making progress toward task completion.

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.

"With external tools, large language models (LLMs) can tackle complex tasks through sustained environmental interaction."

Evaluation Modes

missing

None explicit

Validate eval design from full paper text.

"With external tools, large language models (LLMs) can tackle complex tasks through sustained environmental interaction."

Quality Controls

missing

Not reported

No explicit QC controls found.

"With external tools, large language models (LLMs) can tackle complex tasks through sustained environmental interaction."

Benchmarks / Datasets

missing

Not extracted

No benchmark anchors detected.

"With external tools, large language models (LLMs) can tackle complex tasks through sustained environmental interaction."

Reported Metrics

missing

Not extracted

No metric anchors detected.

"With external tools, large language models (LLMs) can tackle complex tasks through sustained environmental interaction."

Benchmarks and datasets

No benchmark or dataset names were extracted from the available abstract.

Reported metrics

No metric terms were extracted from the available abstract.

Human feedback details
Uses human feedback
No
Feedback types
None
Rater population
Not reported
Expertise required
General
Evaluation details
Evaluation modes
None
Agentic eval
Tool Use, Long Horizon
Quality controls
Not reported
Evidence quality
Low
Use this page as
Background context only

Research brief

Metadata summary

With external tools, large language models (LLMs) can tackle complex tasks through sustained environmental interaction.

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

Key takeaways

  • With external tools, large language models (LLMs) can tackle complex tasks through sustained environmental interaction.
  • LLM-based agents are therefore increasingly used to tackle complex long-horizon tasks in real-world environments.
  • However, these tasks involve sequential, interdependent decisions and changes in environmental states.

Researcher actions

  • Compare this paper against nearby papers in the same arXiv category before using it for protocol decisions.
  • Validate inferred eval signals (Long-horizon tasks) against the full paper.
  • 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

Contribution summary

  • LLM-based agents are therefore increasingly used to tackle complex long-horizon tasks in real-world environments.
  • To address this challenge, we propose Reflection-Guided Policy Optimization (ReflectPO), a reinforcement learning framework for autonomous error recovery in long-horizon tasks.
  • Experiments over two long-horizon tool-use benchmarks show that ReflectPO outperforms multiple existing baselines in most domains.

Why it matters for eval

  • LLM-based agents are therefore increasingly used to tackle complex long-horizon tasks in real-world environments.
  • Experiments over two long-horizon tool-use benchmarks show that ReflectPO outperforms multiple existing baselines in most domains.

Researcher checklist

  • Human feedback protocol is explicit

    No explicit human feedback protocol detected.

  • Evaluation mode is explicit

    No clear evaluation mode extracted.

  • Quality control reporting appears

    No calibration/adjudication/IAA control explicitly detected.

  • Benchmark or dataset anchors are present

    No benchmark/dataset anchor extracted from abstract.

  • Metric reporting is present

    No metric terms extracted.