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README.md

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 license: mit
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-🚀 SWE-Dev, an open-source Agent for Software Engineering tasks!
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-💡 We develop a comprehensive pipeline for creating developer-oriented datasets from GitHub repositories, including issue tracking, code localization, test case generation, and evaluation.
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-🔧 Based on open-source frameworks (OpenHands) and models, SWE-Dev-7B and 32B achieved solve rates of 23.4% and 36.6% on SWE-bench-Verified, respectively, even approaching the performance of GPT-4o. 
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-📚 We find that training data scaling and inference scaling can both effectively boost the performance of models on SWE-bench. Moreover, higher data quality further improves this trend when combined with reinforcement fine-tuning (RFT). For inference scaling specifically, the solve rate on SWE-Dev increased from 34.0% at 30 rounds to 36.6% at 75 rounds.
+📝 [Paper](https://arxiv.org/abs/2506.07636) | 🌐 [Github](https://github.com/THUDM/SWE-Dev/)
 
+- 🤗 [SWE-Dev-7B (Qwen-2.5-Coder-7B-Instruct)](https://huggingface.co/THUDM/SWE-Dev-7B/)
+- 🤗 [SWE-Dev-9B (GLM-4-9B-Chat)](https://huggingface.co/THUDM/SWE-Dev-9B/)
+- 🤗 [SWE-Dev-32B (Qwen-2.5-Coder-32B-Instruct)](https://huggingface.co/THUDM/SWE-Dev-32B/)
+- 🤗 [SWE-Dev-train (Training Data)](https://huggingface.co/datasets/THUDM/SWE-Dev-train/)
 
-SWE-Dev-9B is trained from [glm-4-9B-chat](https://huggingface.co/THUDM/glm-4-9b-chat/)
+🚀 SWE-Dev, an open-source Agent for Software Engineering tasks! This repository contains the SWE-Dev-32B model as presented in the paper [SWE-Dev: Building Software Engineering Agents with Training and Inference Scaling](https://huggingface.co/papers/2506.07636).
 
+💡 We develop a comprehensive pipeline for creating developer-oriented datasets from GitHub repositories, including issue tracking, code localization, test case generation, and evaluation.
 
-Notion Link: https://ubecwang.notion.site/1bc32cf963e080b2a01df2895f66021f?v=1bc32cf963e0810ca07e000c86c4c1e1
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-GitHub Link: https://github.com/THUDM/SWE-Dev
-
-Hugging Face Link: 
+🔧 Based on open-source frameworks (OpenHands) and models, SWE-Dev-7B and 32B achieved solve rates of 23.4% and 36.6% on SWE-bench-Verified, respectively, even approaching the performance of GPT-4o. 
 
-- SWE-Dev-7B (Qwen-2.5-Coder-7B-Instruct): https://huggingface.co/THUDM/SWE-Dev-7B/
-- SWE-Dev-9B (GLM-4-9B-Chat): https://huggingface.co/THUDM/SWE-Dev-9B/
-- SWE-Dev-32B (Qwen-2.5-Coder-32B-Instruct): https://huggingface.co/THUDM/SWE-Dev-32B/
-- SWE-Dev-train: https://huggingface.co/datasets/THUDM/SWE-Dev-train/
+📚 We find that training data scaling and inference scaling can both effectively boost the performance of models on SWE-bench. Moreover, higher data quality further improves this trend when combined with reinforcement fine-tuning (RFT). For inference scaling specifically, the solve rate on SWE-Dev increased from 34.0% at 30 rounds to 36.6% at 75 rounds.