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HFEPX · Eval paper review

SWE-Master: Unleashing the Potential of Software Engineering Agents via Post-Training

Huatong Song, Lisheng Huang, Shuang Sun, Jinhao Jiang +10 more

Published

Feb 3, 2026

Citations

0

Trust level

Low

Usefulness score

0/100 (Low)

Extraction confidence

25% (Low)

Derived from extracted protocol signals and abstract evidence.

Rater population

Not reported

Signals refreshed

Feb 24, 2026

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Tags

Long Horizon

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

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

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

In this technical report, we present SWE-Master, an open-source and fully reproducible post-training framework for building effective software engineering agents. SWE-Master systematically explores the complete agent development pipeline, including teacher-trajectory synthesis and data curation, long-horizon SFT, RL with real execution feedback, and inference framework design. Starting from an open-source base model with limited initial SWE capability, SWE-Master demonstrates how systematical optimization method can elicit strong long-horizon SWE task solving abilities. We evaluate SWE-Master on SWE-bench Verified, a standard benchmark for realistic software engineering tasks. Under identical experimental settings, our approach achieves a resolve rate of 61.4\% with Qwen2.5-Coder-32B, substantially outperforming existing open-source baselines. By further incorporating test-time scaling~(TTS) with LLM-based environment feedback, SWE-Master reaches 70.8\% at TTS@8, demonstrating a strong performance potential. SWE-Master provides a practical and transparent foundation for advancing reproducible research on software engineering agents. The code is available at https://github.com/RUCAIBox/SWE-Master.

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.

"In this technical report, we present SWE-Master, an open-source and fully reproducible post-training framework for building effective software engineering agents."

Evaluation Modes

missing

None explicit

Validate eval design from full paper text.

"In this technical report, we present SWE-Master, an open-source and fully reproducible post-training framework for building effective software engineering agents."

Quality Controls

missing

Not reported

No explicit QC controls found.

"In this technical report, we present SWE-Master, an open-source and fully reproducible post-training framework for building effective software engineering agents."

Benchmarks / Datasets

partial

SWE Bench, SWE Bench Verified

Useful for quick benchmark comparison.

"We evaluate SWE-Master on SWE-bench Verified, a standard benchmark for realistic software engineering tasks."

Reported Metrics

missing

Not extracted

No metric anchors detected.

"In this technical report, we present SWE-Master, an open-source and fully reproducible post-training framework for building effective software engineering agents."

Benchmarks and datasets

SWE-benchSWE-bench Verified

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
Unit of annotation
Trajectory (inferred)
Expertise required
Coding
Evaluation details
Evaluation modes
None
Agentic eval
Long Horizon
Quality controls
Not reported
Evidence quality
Low
Use this page as
Background context only

Research brief

Metadata summary

In this technical report, we present SWE-Master, an open-source and fully reproducible post-training framework for building effective software engineering agents.

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

Key takeaways

  • In this technical report, we present SWE-Master, an open-source and fully reproducible post-training framework for building effective software engineering agents.
  • SWE-Master systematically explores the complete agent development pipeline, including teacher-trajectory synthesis and data curation, long-horizon SFT, RL with real execution feedback, and inference framework design.
  • Starting from an open-source base model with limited initial SWE capability, SWE-Master demonstrates how systematical optimization method can elicit strong long-horizon SWE task solving abilities.

Researcher actions

  • Compare this paper against others mentioning SWE-bench.
  • Validate inferred eval signals (Simulation environment, 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

SWE-bench

Contribution summary

  • In this technical report, we present SWE-Master, an open-source and fully reproducible post-training framework for building effective software engineering agents.
  • SWE-Master systematically explores the complete agent development pipeline, including teacher-trajectory synthesis and data curation, long-horizon SFT, RL with real execution feedback, and inference framework design.
  • We evaluate SWE-Master on SWE-bench Verified, a standard benchmark for realistic software engineering tasks.

Why it matters for eval

  • In this technical report, we present SWE-Master, an open-source and fully reproducible post-training framework for building effective software engineering agents.
  • We evaluate SWE-Master on SWE-bench Verified, a standard benchmark for realistic software engineering tasks.

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

    Detected: SWE-bench, SWE-bench Verified

  • Metric reporting is present

    No metric terms extracted.