Unverified50% confidenceFactExact time
Compared with baselines (such as using only target data, simple data mixing, etc.), DFQI shows advantages in both policy return and stability.
1
Sources
50%
Confidence
Long-term
Relevance
11/3/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedTheoretical analysis shows that, under certain regularity conditions, the target Q-function estimation and policy value convergence rates of DFQI are comparable to estimators that use only target data, while it can effectively exploit source data to reduce variance.76% similarUnverifiedThe authors establish statistical guarantees for DFQI's target estimation and policy learning in a stationary discounted setting, covering both homogeneous and heterogeneous transition kernels.72% similarUnverifiedDirect preference optimization (DPO) shows that under a specific reward structure, the RL objective can be equivalently transformed into likelihood maximization over a certain target distribution, thereby bypassing an explicit reward model and policy gradients.70% similarUnverifiedDPO uses the log-probability difference between the policy model and the reference model as an implicit reward, enabling the model to increase the generation probability of better responses while staying close to the reference model.69% similarUnverifiedIn multiple simulated environments, when target data are limited, DFQI significantly improves policy learning performance by transferring source information.68% similar
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