Papers by Wei-Jin Park
LLM-Based Offline Learning for Embodied Agents via Consistency-Guided Reward Ensemble (2024.findings-emnlp)
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| Challenge: | Employing large language models (LLMs) to enable embodied agents has become popular, yet it presents several limitations in practice. |
| Approach: | They propose a consistency-guided reward ensemble framework to train agents offline via offline reinforcement learning (RL) they use spatio-temporally consistent rewards to derive domain-grounded rewards from training datasets. |
| Outcome: | The proposed framework outperforms state-of-the-art LLM-based agents with 8B parameters and has 117M parameters for agent policy network and only for training. |