Papers by Yuxian Jiang

2 papers
Data Efficient RLVR via Off-Policy Influence Guidance (2026.acl-long)

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Challenge: Existing data selection methods for RLVR are heuristic-based, lacking theoretical guarantees and generalizability.
Approach: They propose an off-policy influence estimation method that approximates data influence using offline trajectories.
Outcome: The proposed method reduces the computational cost of policy rollouts and improves storage and computation efficiency.
Evolutionary Guided Decoding: Iterative Value Refinement for LLMs (2026.acl-long)

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Challenge: Existing methods for directing language model outputs are limited in their accuracy due to a distributional gap . existing methods train static value functions on trajectories sampled exclusively from the base policy .
Approach: They propose a framework to bridge a distributional gap in the accuracy of value functions . they propose RLHF to align language models with human values and task requirements .
Outcome: The proposed framework reduces computational costs and improves value function accuracy by leveraging principled value function optimization.

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