Unverified50% confidenceFactExact time
GUIDER's joint training belongs to the on-policy reinforcement learning paradigm, with process rewards recomputed as the policy updates.
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50%
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Long-term
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10/30/2026
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UnverifiedThe training data for on-policy distillation is generated by the interaction of the student's current policy with the environment, and the state distribution changes as the student policy is updated, in line with the DAgger idea, which can mitigate distribution shift.78% similarUnverified针对工具调用的强化学习训练通常采用执行反馈机制，将实际执行的成功/失败信号作为奖励，可大规模自动化77% similarUnverifiedOnline adaptation methods leverage meta-learning or rapid fine-tuning mechanisms to enable policies to dynamically adjust based on real-environment feedback from the early stages of deployment.76% similarVerifiedBehavior cloning transforms policy learning into a supervised learning problem, using environment states as inputs and expert actions as labels to train a mapping function from states to actions.75% similarUnverifiedIn the Together Post-Training service, the training methods support supervised fine-tuning and reinforcement learning, and the custom models are owned by the customer.73% similar
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