Verified65% confidenceFactExact time
OpenAI's o-series (o1, o3) are specifically reinforcement-trained for chain-of-thought reasoning capabilities, incorporating Monte Carlo Tree Search (MCTS) and process reward models.
3
Sources
65%
Confidence
Long-term
Relevance
7/18/2026
First Seen
Sources
Related Claims
UnverifiedOpenAI从o1到o3的跨越代表了更大幅度的架构或训练范式变化，与Anthropic的渐进式迭代策略形成对比73% similarUnverifiedOpenAI's new method pushes CoT down from the prompt layer to the training and runtime mechanism layer: the model internally generates multiple candidate reasoning paths, filters them through self-consistency checks or a process reward model, and then outputs the final answer.73% similarUnverifiedOpenAI trains models to generate reasoning chains in grug style, omitting unimportant words to reduce token consumption, and users typically cannot see these reasoning traces under normal circumstances.73% similarUnverifiedo3-mini-high has been trained with reinforcement learning for reasoning, achieving reasoning capabilities close to o1 at a smaller scale.71% similarUnverifiedCompanies such as OpenAI and Anthropic collect preference ranking data from human annotators on model outputs, train reward models, and then use reinforcement learning algorithms such as PPO to fine-tune language models for safety alignment.70% similar
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