sijuntan

We introduce *`DeepSWE-Preview`*, a reasoning-enabled coding agent trained from `Qwen3-32B` with only reinforcement learning (RL). It achieves an impressive 59.0*%* on SWE-Bench-Verified with test-time scaling, reaching SOTA for open-weight coding agents (*42.2%* Pass@1, *71.0%* Pass@16).

DeepSWE is trained using [*rLLM*](https://www.notion.so/rLLM-A-Framework-for-Post-Training-Lan...), our framework for post-training language agents. We’ve *open sourced* everything—our dataset, code, training, and eval logs, for everyone to progress on scaling and improving agents with RL.

Through a joint collaboration between the Agentica team and Together AI, we release DeepCoder-14B-Preview, a code reasoning model finetuned from Deepseek-R1-Distilled-Qwen-14B via distributed RL. It achieves an impressive 60.6% Pass@1 accuracy on LiveCodeBench (+8% improvement), matching the performance of o3-mini-2025-01-031 (Low) and o1-2024-12-17 with just 14B parameters. We’ve open-sourced our dataset, code, training logs, and systems optimizations for everyone to progress on scaling and accelerating intelligence with RL.

Through a joint collaboration between the Agentica team and Together AI, we release DeepCoder-14B-Preview, a code reasoning model finetuned from Deepseek-R1-Distilled-Qwen-14B via distributed RL. It achieves an impressive 60.6% Pass@1 accuracy on LiveCodeBench (+8% improvement), matching the performance of o3-mini-2025-01-031 (Low) and o1-2024-12-17 with just 14B parameters. We’ve open-sourced our dataset, code, training logs, and systems optimizations for everyone to progress on scaling and accelerating intelligence with RL.