arXiv Machine Learning
SWE-Spot: Building Small Repo-Experts with Repository-Centric Learning
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arXiv AI
Sep 21
CodeMidas: Scaling Agentic Coding RL Environments from Code Itself
arXiv:2609.22068v1 Announce Type: new Abstract: Training capable coding agents via reinforcement learning (RL) requires diverse tasks with reliable verifiers. Open-source codebases offer a rich sourc...
arXiv AI
Jun 26
NebulaExp-8B: An Empirical Post-Training Pipeline via Full-Scale Ablation Research
arXiv:2606. 26671v1 Announce Type: new Abstract: Post-training alignment determines the reasoning and human preference following capabilities of large language models, yet most existing works withhold detailed data construction, filtering rules and training recipes, which hinders community reproducibility and lightweight model optimization.
arXiv AI
Aug 7
CodeGrep: An RL-Trained Retrieval Agent for LLM Coding Agents
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.
arXiv AI
Oct 12
SWE-Critix: Post-Training Generalist Verifiers for Assessing Coding-Agent Trajectories
SWE-Critix is a post‑training pipeline that creates generalist verifiers for coding‑agent trajectories. It uses supervised fine‑tuning followed by reinforcement learning to train 30B models on CodeForge‑Preview data, enabling the verifier to predict completion labels and generate natural‑language explanations. The approach raises classification accuracy from 57% to 78% on a held‑out test set.
Hugging Face Trending Papers
Aug 6
CodeGrep: An RL-Trained Retrieval Agent for LLM Coding Agents
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. On SWE-Bench Verified, a 30B OpenHands agent averages 23 rounds and 631K tokens per resolved issue, with many calls spent on grep, glob, and view_file during repository exploration.
arXiv AI
Jun 6
Code2LoRA: Hypernetwork-Generated Adapters for Code Language Models under Software Evolution
arXiv:2606. 06492v1 Announce Type: cross Abstract: Code language models need repository-level context to resolve imports, APIs, and project conventions.