Unverified70% confidenceOpinionTime unknown
Models trained with reinforcement learning (RL) outperform models trained solely with SFT on Agent tasks.
2
Sources
70%
Confidence
Long-term
Relevance
6/1/2026
First Seen
Sources
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Related Claims
UnverifiedIn the Agent context, reinforcement learning (RL) is mainly used to optimize decision-making policies, with commonly used methods including RLHF and DPO; compared with supervised fine-tuning, it can explore better paths through trial and error, but it faces challenges such as difficulty in reward design, low sample efficiency, and unstable training.78% similarVerified当代前沿模型的训练方法包括RLHF（基于人类反馈的强化学习）及其后继者RLAIF和过程奖励模型（PRM）75% similarUnverifiedSpecialized super-resolution models carefully trained for specific domains often outperform general-purpose models.75% similarVerifiedThrough Agent Tuning specialized training, small-parameter models such as 3B and 7B can also acquire strong Agent capabilities.75% similarUnverifiedAgima's training is divided into two stages: the first stage uses supervised fine-tuning (SFT) to unify the policy and the world model into a single omni-model; the second stage uses agentic reinforcement learning (RL) to teach the model to reason over imagined futures and use them for planning.74% similar
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