E2CL: Exploration-based Error Correction Learning for Embodied Agents (2024.findings-emnlp)
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| Challenge: | Language models are exhibiting increasing capability in knowledge utilization and reasoning, but they often suffer from misalignment between their intrinsic knowledge and environmental knowledge, leading to infeasible actions. |
| Approach: | They propose a framework that leverages exploration-induced errors and environmental feedback to enhance environment alignment for embodied agents. |
| Outcome: | The proposed framework outperforms baseline methods and exhibits superior self-correction capabilities. |
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| Challenge: | Existing methods assume that large language models have a complete understanding of their environment, overlooking potential gaps in their grasp of actual world dynamics. |
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| Challenge: | Large Language Models (LLMs) have become integral components in various autonomous agent systems. |
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| Challenge: | Existing methods to improve text-based reinforcement-learning agents' performance contain failed actions that reinforce incorrect behaviors and reduce task success rates. |
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| Challenge: | Existing RL agents are far away from solving text-based games due to their combinatorially large action spaces that hinders efficient exploration. |
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| Challenge: | Employing large language models (LLMs) to enable embodied agents has become popular, yet it presents several limitations in practice. |
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| Challenge: | Embodied Instruction Following has shown an impressive success rate when the environment has been seen in training, but when deployed in an unseen environment, it tends to struggle when deployed with an unsightly environment. |
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Kinjal Basu, Keerthiram Murugesan, Subhajit Chaudhury, Murray Campbell, Kartik Talamadupula, Tim Klinger
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| Challenge: | Existing Language Agents rely on a fixed mechanism or a set of mechanisms activated in a predefined order, limiting their adaptation to varied potential task solution structures. |
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E3-TIR: Enhanced Experience Exploitation for Tool-Integrated Reasoning (2026.findings-acl)
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| Challenge: | Existing training paradigms for Large Language Models (LLMs) suffer from inefficient exploration and mode degradation due to a lack of prior guidance, while SFT-then-RL is limited by high data costs and capability plateaus caused by low-entropy collapse. |
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