AgentPro: Enhancing LLM Agents with Automated Process Supervision (2025.emnlp-main)
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| Challenge: | Existing frameworks lack explicit supervision during the reasoning process, which may lead to error propagation across reasoning chains. |
| Approach: | They propose a framework which automates process supervision for large language model agents by automatically generating step-level annotations and developing a process reward model based on these annotations. |
| Outcome: | The proposed framework outperforms existing agent-based methods on four datasets and achieves a 6.32% increase in accuracy. |
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| Challenge: | Recent approaches to enhance agent performance focus on outcome rewards, which may lead to errors or suboptimal actions due to the absence of process supervision signals. |
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| Challenge: | Existing reasoning methods excel in structured domains like math and code, but they are not all effective in knowledge-intensive tasks. |
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| Challenge: | Existing studies focus on prompt engineering or framework scheduling of one/multiple LLMs. |
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Luca Gioacchini, Giuseppe Siracusano, Davide Sanvito, Kiril Gashteovski, David Friede, Roberto Bifulco, Carolin Lawrence
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| Challenge: | Existing methods for ensembling language models fail to address complex reasoning tasks. |
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