Together AI releases Deeps depth: The open-based source opened for open source is based on the QWEN3-3B and up to 59% in SweBench

In partnership AI has released the Deention, open software agent, with full opened opened, full-trained surface by strengthening the strengthening (RL). Designed on top of QWEN3-32-language model, Deepatted Accessing 59% accuracy of a 52% of SweBench and 42.2% Pass @ 1, enter the main board between high models. This opening symbolizes the important AI, from pipes for prestics to build the private agents that are learning and develop the real world response.

Emphasis learning to meet the code generation

Deepwere is the result of training after the QWen3-3B foundation using RLFM, the strengthening framework of Agentica is designed for the agents of language. Unlike the common ways of good guidance, the RLLM enables agents to adapt to the functioning of the real work in the world. Deentials are directly trained to solve the sophisticated software software software uses using a loop driven by feedback rather than static dasets.

Training pipe includes Agentica's R2mm Dataset – Software Engineering Benchmark designed for the development of an RL agent. The framework focuses on the Model of the Court of Training Objectives, such as fixing bedbugs, completing activities, and planning code, rather than predicting the following token submission. This alignment is very deep and how the engineers are learned and learn from the results.

Reaches for work and skills

In Sweenbench-reinforced of the software engineering agents, the deepest 59% scores with time test. This is well putting the model models of open mass. In Pass @ 1 to explore – which measures the likelihood that an agent solves the problem properly in the first deeply depth.

This results in emphasizing RL-based training capacity to expand the performance of Agentic, especially at domains that require effective thinking and specific effects, such as the constipation of the code. The construction of model, inherited from qwen3-32B, enables you to measure successful measures while it is ready for the world's actual apps.

Open Source and Recovery in its spine

One of the features of this release is its full look. In cooperation with AI and Agentica and unlocks not only the depth model but also all the training recipe, including RLFM frame, R2lmmm DRCREPS, and Training Scriptures. This improves reconstruction and inviting comprehensive research and developer communities to extend or build on depth without limits.

Developers can reach depths and RLLM for the following:

From the grounds of language to language agents

Deention is putting Shipping philosophical and active shifts: From language construction models and to create agents studying the connection. Traditional LLMS shows solid consultation skills, but usually does not have formators to adapt or improve with use. Emphasizing learning makes these models unable to only be presented by the introduction but to improve over time, agree to the distribution of new problems and backgrounds.

This approach also allowed the Department of Local Service. Because Deentiewe is completely open with Modar source, it can be extended and reeveloped for disaster risk assessment. The engineers and investigators can create their agents above the Deention has used RLFm to use various backgrounds such as the web wander, robotic, or financial aid.

Store

Deepwere is a milestone in the self-repentance of the software Engineering. Through strengthening the main models such as QWEN3-3B and release all training infrastructure, together AI empowering the future when agents are just available and trained. This jumping from languages ​​is active in a practical agency than important results throughout the program, automated, and intelligent system formation.

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