Collectively AI has launched DeepSWE, a state-of-the-art, absolutely open-sourced software program engineering agent that’s skilled fully by reinforcement studying (RL). Constructed on prime of the Qwen3-32B language mannequin, DeepSWE achieves 59% accuracy on the SWEBench-Verified benchmark and 42.2% Cross@1, topping the leaderboard amongst open-weight fashions. This launch represents a major shift for Collectively AI, from conventional pretraining pipelines towards creating autonomous language brokers that repeatedly study and enhance by way of real-world suggestions.
Reinforcement Studying Meets Code Technology
DeepSWE is the results of post-training the Qwen3-32B basis mannequin utilizing rLLM, Agentica’s modular reinforcement studying framework tailor-made for language brokers. Not like typical supervised fine-tuning approaches, rLLM allows brokers to adapt to real-world workflows by expertise. DeepSWE has been particularly skilled to unravel complicated software program engineering duties utilizing a feedback-driven loop somewhat than static datasets.
The coaching pipeline incorporates Agentica’s R2EGym dataset—a software program engineering benchmark designed for RL-style agent growth. The framework focuses on coaching language fashions with action-oriented targets, similar to fixing bugs, finishing capabilities, and enhancing code, somewhat than merely predicting next-token distributions. This aligns DeepSWE extra intently with how human engineers iterate and study from outcomes.
Efficiency Benchmarks and Capabilities
On SWEBench-Verified, probably the most rigorous benchmark for software program engineering brokers, DeepSWE scores 59% with test-time scaling. This considerably outperforms earlier open-weight fashions. In Cross@1 evaluations—which measure the likelihood that the agent solves an issue accurately on the primary try—DeepSWE reaches a formidable 42.2%.
These outcomes underscore the ability of RL-based coaching in enhancing agentic conduct, notably in domains requiring iterative reasoning and exact outputs, similar to code synthesis. The mannequin’s structure, inherited from Qwen3-32B, allows it to scale successfully whereas remaining appropriate for real-world functions.
Open Supply and Reproducibility at Its Core
One of many standout options of this launch is its full transparency. Collectively AI and Agentica have open-sourced not solely the DeepSWE mannequin but in addition all the coaching recipe, together with the rLLM framework, the R2EGym dataset, and coaching configuration scripts. This promotes reproducibility and invitations the broader analysis and developer communities to increase or construct upon DeepSWE with out restrictions.
Builders can entry DeepSWE and rLLM by way of the next:
From Language Reasoners to Language Brokers
DeepSWE marks a philosophical and sensible shift: from constructing fashions that cause about language to constructing brokers that study by interplay. Conventional LLMs have proven sturdy reasoning capabilities, however usually lack the power to adapt to suggestions or enhance with use. Reinforcement studying allows these fashions to not solely carry out nicely at launch however to get higher over time, adapting to new drawback distributions and domains.
This method additionally opens the door for native deployment. As a result of DeepSWE is absolutely open-source and modular, it may be prolonged and retrained for organization-specific use instances. Builders and researchers can construct their very own brokers on prime of DeepSWE utilizing rLLM to serve numerous domains similar to net navigation, robotics, or autonomous analysis help.
Conclusion
DeepSWE is a milestone within the evolution of generative AI for software program engineering. By making use of reinforcement studying to giant language fashions like Qwen3-32B and releasing all the coaching infrastructure, Collectively AI is enabling a future the place brokers aren’t simply pretrained and deployed, however frequently skilled and improved. This leap from language understanding to action-oriented company has important implications throughout programming, automation, and clever system design.
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