DeepSeek Unveils SPCT for Scaling Inference and Teases Next-Gen R2 Model
DeepSeek AI has published a research paper introducing a novel technique called SPCT, designed to enhance the scalability of General Reward Models (GRMs) during the inference phase. This development coincides with hints regarding the imminent release of their next-generation model, R2. The paper, titled 'Inference-Time Scaling for Generalist Reward Modeling,' details how GRMs can optimize reward generation by dynamically producing principles and critiques through rejection fine-tuning and rule-based online reinforcement learning. This approach aligns with a broader industry shift from pre-training scaling to post-training and inference optimization, similar to strategies employed by OpenAI’s o1 model. By leveraging increased computational effort during testing, DeepSeek aims to improve reasoning capabilities and long-term planning in Large Language Models (LLMs). The article highlights the synergistic relationship between LLMs and reinforcement learning, noting that while LLMs provide foundational understanding, RL enhances decision-making. This advancement addresses challenges like reward sparsity and seeks to establish new scaling laws for reinforcement learning in AI, marking a significant step toward more systematic and intelligent agent capabilities.
Wire timeline
DeepSeek Unveils SPCT for Scaling Inference and Teases Next-Gen R2 Model
DeepSeek AI has published a research paper introducing a novel technique called SPCT, designed to enhance the scalability of General Reward Models (GRMs) during the inference phase. This development coincides with hints regarding the imminent release of their next-generation model, R2. The paper, titled 'Inference-Time Scaling for Generalist Reward Modeling,' details how GRMs can optimize reward generation by dynamically producing principles and critiques through rejection fine-tuning and rule-based online reinforcement learning. This approach aligns with a broader industry shift from pre-training scaling to post-training and inference optimization, similar to strategies employed by OpenAI’s o1 model. By leveraging increased computational effort during testing, DeepSeek aims to improve reasoning capabilities and long-term planning in Large Language Models (LLMs). The article highlights the synergistic relationship between LLMs and reinforcement learning, noting that while LLMs provide foundational understanding, RL enhances decision-making. This advancement addresses challenges like reward sparsity and seeks to establish new scaling laws for reinforcement learning in AI, marking a significant step toward more systematic and intelligent agent capabilities.
Synced