DeepScaleR-1.5B is an innovative open-source model capable of delivering performance on par with OpenAI’s flagship model for reasoning tasks and has shown even stronger results in several benchmarks. Developed by the Agentica team, which operates under the Berkeley AI Research and Sky Computing Lab, DeepScaleR-1.5B-Preview has demonstrated exceptional skills, particularly in benchmarks like AIME2024, a selective test based on a prestigious mathematics competition.
The training of DeepScaleR involved the processing of 40,000 mathematical problems with a significant commitment of resources—3,800 A100 GPU hours. A distinguishing feature of this model is its iterative context lengthening strategy, which begins training with shorter context windows and gradually shifts to longer ones, improving both efficiency and effectiveness. The model’s architecture leverages an Outcome Reward Model (ORM) rather than a traditional Process Reward Model (PRM), progressively increasing the context length from 8K to 24K tokens.
The significance of DeepScaleR’s performance is underscored by its achievement of a 43.1% accuracy rate in solving AIME problems—an impressive 14.3% increase over its predecessor. This result illustrates the potential of combining high-quality supervised fine-tuning (SFT) with reinforcement learning (RL) techniques to enhance reasoning capabilities in large language models.
In terms of implementation, running DeepScaleR on DigitalOcean’s infrastructure is straightforward. Users can employ high-performance inference systems, such as vLLM, to deploy this model efficiently. To get started, one can create a GPU droplet and configure it in a Jupyter Notebook environment, allowing for seamless integration and experimentation.
Utilizing this advanced model involves initializing the DeepScaleR within the inference engine, setting up sampling parameters, and generating results from input prompts. This process fosters experimentation and encourages users to explore the potential of logical reasoning and problem-solving applications that can be derived from DeepScaleR.
In conclusion, DeepScaleR not only showcases the advancements in AI model capabilities but also provides a valuable resource for researchers and developers looking to tap into cutting-edge technology for various applications.
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