Public articles linked to the same research event.
arXiv The authors introduce a two-stage training framework around colgrep, a local semantic search tool, combining supervised fine-tuning with turn-level credit based on retrieval outcomes and reinforcement learning on localization quality, and train three 1–1.7B-parameter models for file localization; on SWE-bench Lite and Multi-SWE-bench Flash, colgrep-equipped agents outperform matched grep agents at every training stage, with Qwen3-1.7B reaching 67.29 and 54.00 file F1 after weighted SFT plus RL, while using 29.1% fewer tokens and reducing end-to-end CPU latency by 44.1% and transferring better to seven languages unseen during fine-tuning.
The authors introduce a two-stage training framework around colgrep, a local semantic search tool, combining supervised fine-tuning with turn-level credit based on retrieval outcomes and reinforcement learning on localization quality, and train three 1–1.7B-parameter models for file localization; on SWE-bench Lite and Multi-SWE-bench Flash, colgrep-equipped agents outperform matched grep agents at every training stage, with Qwen3-1.7B reaching 67.29 and 54.00 file F1 after weighted SFT plus RL, while using 29.1% fewer tokens and reducing end-to-end CPU latency by 44.1% and transferring better to seven languages unseen during fine-tuning.
The authors introduce a two-stage training framework around colgrep, a local semantic search tool, combining supervised fine-tuning with turn-level credit based on retrieval outcomes and reinforcement learning on localization quality, and train three 1–1.7B-parameter models for file localization; on SWE-bench Lite and Multi-SWE-bench Flash, colgrep-equipped agents outperform matched grep agents at every training stage, with Qwen3-1.7B reaching 67.29 and 54.00 file F1 after weighted SFT plus RL, while using 29.1% fewer tokens and reducing end-to-end CPU latency by 44.1% and transferring better to seven languages unseen during fine-tuning.
The authors introduce a two-stage training framework around colgrep, a local semantic search tool, combining supervised fine-tuning with turn-level credit based on retrieval outcomes and reinforcement learning on localization quality, and train three 1–1.7B-parameter models for file localization; on SWE-bench Lite and Multi-SWE-bench Flash, colgrep-equipped agents outperform matched grep agents at every training stage, with Qwen3-1.7B reaching 67.29 and 54.00 file F1 after weighted SFT plus RL, while using 29.1% fewer tokens and reducing end-to-end CPU latency by 44.1% and transferring better to seven languages unseen during fine-tuning.