Duplicate from togethercomputer/CoderForge-Preview

Co-authored-by: Alpay Ariyak <alpayariyak@users.noreply.huggingface.co>

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README.md

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+---
+dataset_info:
+- config_name: trajectories
+  features:
+  - name: trajectory_id
+    dtype: string
+  - name: finish_reason
+    dtype: string
+  - name: image
+    dtype: string
+  - name: messages
+    dtype: string
+  - name: reward
+    dtype: float64
+  - name: tools
+    dtype: string
+  - name: license
+    dtype: string
+  splits:
+  - name: SWE_Rebench
+    num_bytes: 19392208677
+    num_examples: 77169
+  - name: SWE_Smith
+    num_bytes: 33088967556
+    num_examples: 148001
+  - name: R2E_Gym
+    num_bytes: 6869123922
+    num_examples: 32964
+  - name: filtered_reward1
+    num_bytes: 33547502194
+    num_examples: 155144
+  download_size: 22788997561
+  dataset_size: 92897802349
+- config_name: trajectories-tokenized_qwencoder
+  features:
+  - name: trajectory_id
+    dtype: string
+  - name: reward
+    dtype: float64
+  - name: chat_template_applied
+    dtype: string
+  - name: input_ids
+    list: int32
+  - name: labels
+    list: int64
+  splits:
+  - name: SWE_Rebench
+    num_bytes: 64238782798
+    num_examples: 77169
+  - name: SWE_Smith
+    num_bytes: 107118447512
+    num_examples: 148001
+  - name: R2E_Gym
+    num_bytes: 23869485518
+    num_examples: 32964
+  - name: filtered_reward1
+    num_bytes: 108349044091
+    num_examples: 155144
+  download_size: 49985669802
+  dataset_size: 303575759919
+configs:
+- config_name: trajectories
+  data_files:
+  - split: SWE_Rebench
+    path: trajectories/SWE_Rebench-*
+  - split: SWE_Smith
+    path: trajectories/SWE_Smith-*
+  - split: R2E_Gym
+    path: trajectories/R2E_Gym-*
+  - split: filtered_reward1
+    path: trajectories/filtered_reward1-*
+- config_name: trajectories-tokenized_qwencoder
+  data_files:
+  - split: SWE_Rebench
+    path: trajectories-tokenized_qwencoder/SWE_Rebench-*
+  - split: SWE_Smith
+    path: trajectories-tokenized_qwencoder/SWE_Smith-*
+  - split: R2E_Gym
+    path: trajectories-tokenized_qwencoder/R2E_Gym-*
+  - split: filtered_reward1
+    path: trajectories-tokenized_qwencoder/filtered_reward1-*
+---
+
+
+# CoderForge-Preview: SOTA Open Dataset for Training Efficient Agents
+
+**CoderForge-Preview** is **the** **largest open test-verified coding agent dataset.** 
+
+Fine-tuning Qwen-3 32B on it, we boost **SWE-Bench Verified performance** **23.0% → 59.4% pass@1** and rank **#1 among open-data** and **#2 among open-weight models ≤32B parameters.** 
+
+![top_open_data_models](https://cdn-uploads.huggingface.co/production/uploads/63972847b3e2256c9ce1307b/UG0fXsbVAMxoxxuC0kRLe.png)
+
+
+![top_open_weight_models](https://cdn-uploads.huggingface.co/production/uploads/63972847b3e2256c9ce1307b/y6MKx8AQTGkeG4N8kGDqd.png)
+
+
+## Limitations
+- **Adaptability to different scaffolds:** We generated all trajectories using a **single scaffold** and **fixed tool set** (no permutations). Models trained via SFT on this data may perform worse when deployed with **different scaffolds, tools, prompt templates, or tool-call formats**.
+- **Task scope:** Our data sources skew toward **bug fixing**. As a result, models trained on this dataset may be less capable on tasks outside that scope, such as **feature implementation**, **refactors**, or **design-heavy changes**.
+- **User interaction:** Real coding-agent usage often involves **ongoing user collaboration**, with user messages appearing throughout the trajectory—not just at the start. This kind of interactive supervision is still largely missing from open coding-agent datasets (including ours). Models trained on SFT alone may therefore underperform in **interactive settings**.
+
+## Conclusion
+
+In this release, we focus on **large-scale agentic data generation**: assembling **51K distinct open-source tasks** and generating **long-horizon, multi-step SFT trajectories**. Our results show that a simple data-generation pipeline combined with **pure SFT** can produce substantial gains in coding-agent performance.
+
+### Next steps
+Moving forward, we plan to:
+- **Scale data generation further** (more tasks, more trajectories, longer horizons where helpful)
+- Generate data under **multiple scaffolds**, **tool sets**, and **prompt/tool-call permutations** to improve robustness and transfer
+- Train **larger models** and run more systematic **hyperparameter tuning**
+- Follow the **DeepSWE** training paradigm by applying **agentic reinforcement learning** on top of our fine-tuned model to drive further performance gains
+
+
+## Citation
+
+```bibtex
+@misc{CoderForge2026,
+  title        = {CoderForge-Preview: SOTA Open Dataset for Training Efficient Agents},
+  author       = {Ariyak, Alpay and Zhang, Junda and Wang, Junxiong and Zhu, Shang and Bianchi, Federico and Srivastava, Sanjana and Panda, Ashwinee and Bharti, Siddhant and Xu, Chenfeng and Heo, John and Wu, Xiaoxia Shirley and Zhou, James and Liang, Percy and Song, Leon and Zhang, Ce and Athiwaratkun, Ben and Zhou, Zhongzhu and Wu, Qingyang},
+  year         = {2026},
+  month        = feb,
+  publisher    = {TogetherAI Blog},
+  url          = {https://www.together.ai/blog/coderforge-preview},
+  note         = {Project core leads: Alpay Ariyak; Zhongzhu Zhou; Qingyang Wu}
+}
+```

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