Papers with 7B-scale models
SPPD: Self-training with Process Preference Learning Using Dynamic Value Margin (2025.findings-emnlp)
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| Challenge: | Existing approaches to improve numerical and logical reasoning of Large Language Models are limited . existing approaches rely on prompt engineering and pretrained knowledge to ensure correctness . |
| Approach: | They propose to train LLMs with process-based reasoning using a dynamic value margin . they use the Bellman optimality equation to derive a value margin for step-level preference optimization . |
| Outcome: | The proposed method is equivalent to on-policy policy gradient methods under constrained reward functions. |
COAST: Enhancing the Code Debugging Ability of LLMs through Communicative Agent Based Data Synthesis (2025.findings-naacl)
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Weiqing Yang, Hanbin Wang, Zhenghao Liu, Xinze Li, Yukun Yan, Shuo Wang, Yu Gu, Minghe Yu, Zhiyuan Liu, Ge Yu
| Challenge: | Existing code debugging benchmarks focus on the Code Repair stage of the code generation process. |
| Approach: | They propose a framework to evaluate the debugging abilities of large language models by emulating the human debug process. |
| Outcome: | The proposed framework outperforms human-curated and GPT-4-generated training data, enabling 7B-scale LLMs to achieve comparable debugging performance to GPT-3.5. |
SMART: Self-Aware Agent for Tool Overuse Mitigation (2025.findings-acl)
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Cheng Qian, Emre Can Acikgoz, Hongru Wang, Xiusi Chen, Avirup Sil, Dilek Hakkani-Tür, Gokhan Tur, Heng Ji
| Challenge: | Current Large Language Models (LLMs) lack self-awareness to balance reasoning and tool use, increasing computational overhead. |
| Approach: | They propose a paradigm that enhances an agent’s self-awareness to optimize task handling and reduce tool overuse. |
| Outcome: | The proposed model reduces tool use by 24% while improving performance by over 37%. |