Verified75% confidenceFactExact time
The o-series models score each intermediate reasoning step individually using a Process Reward Model, rather than relying solely on the correctness of the final result.
3
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75%
Confidence
Long-term
Relevance
7/21/2026
First Seen
Sources
Related Claims
UnverifiedIn LLM post-training, the model directly spews intermediate reasoning steps that should have been hidden to the user, or during RL reward computation, the reward model that should only evaluate the final answer sees the reasoning process, thereby distorting the optimization direction.76% similarUnverifiedIf rewards target only final outcomes, the model tends to take shortcuts and memorize solutions for specific scenarios.76% similarVerifiedThe o-series models combine chain-of-thought reasoning with reinforcement learning, with the model generating internal thinking steps before producing an answer.75% similarUnverifiedIn multi-hop reasoning scenarios, each hop's retrieval should incrementally supplement based on the conclusions of the previous hop, and the decision model can explicitly track the known information set to avoid redundant recall.73% similarVerifiedReward model training can use learning-to-rank frameworks such as the Bradley-Terry model, training a neural network to predict the probability of human preference between any two responses, taking a (prompt, response) pair as input and outputting a scalar score.72% similar
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