Papers by Jiafei Lyu
World Models with Hints of Large Language Models for Goal Achieving (2025.naacl-long)
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| Challenge: | Existing methods address this by adding intrinsic rewards, but they fail to provide meaningful guidance in long-horizon decision-making tasks with large state and action spaces lacking purposeful exploration. |
| Approach: | They propose a multi-modal model-based RL approach that integrates the proposed hinting subgoals into the model rollouts to encourage goal discovery and reaching in challenging tasks. |
| Outcome: | The proposed model outperforms existing methods in challenging, sparse-reward environments such as HomeGrid, Crafter, and Minecraft by 41.8%, 21.1%, and 9.9%. |
VLP: Vision-Language Preference Learning for Embodied Manipulation (2025.emnlp-main)
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| Challenge: | Existing approaches to reward engineering are time-consuming and expensive to collect human preference labels. |
| Approach: | They propose a vision-language preference learning framework which learns from human feedback . they define three types of language-conditioned preferences and construct a visual preference dataset . |
| Outcome: | The proposed framework outperforms baselines on embodied manipulation tasks and can be applied to other tasks. |