Unverified50% confidenceFactExact time
In multiple simulated environments, when target data are limited, DFQI significantly improves policy learning performance by transferring source information.
1
Sources
50%
Confidence
Long-term
Relevance
11/3/2026
First Seen
Sources
Related Entities
Related Claims
VerifiedTransfer learning refers to migrating model parameters pre-trained on large-scale datasets to a specific small-sample task for fine-tuning.79% similarUnverifiedA systematic study, through dense grid experiments, found that DPO's optimal learning rate shifts as model scale changes.77% similarUnverifiedThe paper proposes Direct Transfer Fitted Q-Iteration (DFQI), which directly incorporates source-environment information into target value-function learning through a joint estimation framework.73% 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.71% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1129573API
curl https://kongchang.com/api/v1/knowledge/claims/1129573MCP
get_claim(id=1129573)