SWE-Prime introduces a two-stage supervised fine-tuning data selection process for large language models tackling software issues. The first stage filters entire trajectories by quality and representativeness, while the second stage selects meaningful semantic segments based on contribution, learnability, and risk. Experiments on SWE-Bench Pro and Verified demonstrate that training on just 10% of trajectories chosen by SWE-Prime surpasses full-dataset training, achieving up to 12.2% and 24.2% performance gains.
By Dewu Zheng, Ruizhe Ye, Yanlin Wang, Yang Ye, Hongyu Zhang, Ensheng Shi, Xilin Liu, Yuchi Ma, Jianxing Yu, Zibin Zheng
arXiv:2609.01244v1 Announce Type: new
Abstract: Every supervised fine-tuning run forces the same chain of decisions, such as learning rate, batch size, LoRA or full fine-tuning, how many epochs, whic...
By Charles O'Neill, Mudith Jayasekara, Harry Partridge
SWE-Prime introduces a two‑stage, multi‑granularity supervised fine‑tuning (SFT) data selection process for large language models tackling real‑world software problems. The first stage filters entire trajectories based on process quality, result quality, and representativeness, while the second stage evaluates semantic segments for contribution, learnability, and risk, keeping all segments in context but only penalizing selected ones during training. Experiments on SWE‑Bench Pro and Verified demonstrate that training on just 10% of trajectories chosen by SWE‑Prime outperforms full‑dataset training, achieving up to 12.2% and 24.2% relative gains.
arXiv:2609.18417v1 Announce Type: new
Abstract: Multi-turn agent trajectories often contain redundant rounds (failed tool calls, parallel sub-queries, verification-only steps) that inflate both train...
By Zhuo Chen, Zhen Zhang, Xinyu Wang, Kewei Tu
Every supervised fine-tuning run forces the same chain of decisions, such as learning rate, batch size, LoRA or full fine-tuning, how many epochs, which optimiser, and what data to feed the model. Eac...
arXiv:2607. 20499v1 Announce Type: new Abstract: Large Language Models generate plausible backend code, but a single-pass paradigm provides no guarantee of correctness or runtime reliability.
By Sai Deekshith Lekkala, Jothi Prabha Appadurai, Rohith Reddy Bellibatlu, Manpreet Singh
arXiv:2607. 21612v1 Announce Type: cross Abstract: Parameter-efficient fine-tuning methods like LoRA have become the default for adapting large language models, succeeding across instruction following, style transfer, and factual adaptation.
By Simon Dennis, Kevin Shabahang, Hao Guo, Rivaan Patil
arXiv:2606. 20512v1 Announce Type: cross Abstract: LLM-based coding agents need higher-level operational knowledge about a repository (which files house which subsystems, how to run the test suite, which workflows have historically led to wrong fixes) that does not exist in the code itself.
By Asa Shepard, Jeannie Albrecht
arXiv:2606. 22678v2 Announce Type: replace-cross Abstract: Agentic coding harnesses - such as Agent-Skills, Superpowers, and Agent-Rigor - are increasingly deployed to augment underlying LLMs for real-world software engineering tasks.
By Meher Bhaskar Madiraju, Meher Sai Preetam Madiraju
arXiv:2604. 05336v2 Announce Type: replace Abstract: Models often fail to complete agentic tasks because they lack core capabilities required by the target environment.
By Hangoo Kang, Tarun Suresh, Jon Saad-Falcon, Azalia Mirhoseini
arXiv:2608. 05886v1 Announce Type: cross Abstract: Modern LLM coding agents such as Claude Code and OpenHands share a common inefficiency: they spend much of their token budget finding the file to patch, rather than patching it.
By Wuya Chen, Yihao yang, Yang Cao, Yue Lin
arXiv:2607. 02032v1 Announce Type: new Abstract: Evaluating LLM agents on benchmarks like SWE-Bench and GAIA can be expensive, time-consuming, and requires complex infrastructure.
By Yueqi Song, Lintang Sutawika, Jiarui Liu, Lindia Tjuatja, Jiayi Geng, Yunze Xiao, Daniel Lee, Aditya Bharat Soni, Vincent Lo, Xiang Yue, Graham Neubig