Papers with reinforcement fine-tuning
AgentCPM-GUI: Building Mobile-Use Agents with Reinforcement Fine-Tuning (2025.emnlp-demos)
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Zhong Zhang, Yaxi Lu, Yikun Fu, Yupeng Huo, Shenzhi Yang, Yesai Wu, Han Si, Xin Cong, Haotian Chen, Yankai Lin, Xie Xie, Wei Zhou, Wang Xu, Zhou Su, Zhongwu Zhai, Xiaoming Liu, null Meiyudong, Jianming Xu, Hongyan Tian, Chongyi Wang, Chi Chen, Yuan Yao, Zhiyuan Liu, Maosong Sun
| Challenge: | Large language model agents have enabled GUI-based automation, but their deployment is limited by noisy data, poor generalization, and lack of support for non-English GUIs. |
| Approach: | They propose an 8B-parameter GUI agent built for robust and efficient on-device GUI interaction. |
| Outcome: | The proposed GUI agent achieves promising performance on five public benchmarks and proposed Chinese benchmark CAGUI. |
SATQuest: A Verifier for Logical Reasoning Evaluation and Reinforcement Fine-Tuning of LLMs (2026.acl-long)
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Yanxiao Zhao, Yaqian Li, Zi-Hao Bo, Rinyoichi Takezoe, Haojia Hui, Mo Guang, null Renlei, Xiaolin Qin, Kaiwen Long
| Challenge: | Large language models exhibit strong general reasoning abilities, yet the community lacks controllable, scalable, and verifiable tools to analyze and improve them. |
| Approach: | They propose a verifier that generates diverse SAT-based reasoning tasks from CNF instances and checks answers objectively with PySAT. |
| Outcome: | The proposed verifier generates diverse SAT-based reasoning tasks from CNF instances and checks answers objectively with PySAT. |
PerfCoder: Large Language Models for Interpretable Code Performance Optimization (2026.findings-acl)
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Jiuding Yang, Shengyao Lu, Hongxuan Liu, Shayan Shirahmad Gale Bagi, Zahra Fazel, Tomasz Czajkowski, Di Niu
| Challenge: | Large language models (LLMs) have advanced automatic code generation, but their ability to produce high-performance code remains limited. |
| Approach: | They propose a family of large language models that generate performance-enhanced code through interpretable and customized optimization strategies. |
| Outcome: | The proposed model outperforms existing models on the PIE code performance benchmark and produces interpretable feedback that can guide larger LLMs in a planner–optimizer workflow. |
Distillation Traps and Guards: A Calibration Knob for LLM Distillability (2026.acl-long)
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| Challenge: | Knowledge distillation (KD) transfers capabilities from large language models (LLMs) to smaller students, yet it can fail unpredictably and also underpins model leakage risks. |
| Approach: | They propose a method that allows teachers to control their distillability via reinforcement fine-tuning (RFT) they propose to use tail noise, off-policy instability, and the teacher–student gap to improve KD. |
| Outcome: | The proposed method outperforms SFT and KD baselines and can be used to protect teachers and students from bottlenecks. |
ReRec: Reasoning-Augmented LLM-based Recommendation Assistant via Reinforcement Fine-tuning (2026.acl-long)
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| Challenge: | Existing reasoning-augmented systems that handle complex queries are lacking . we present a framework that enhances LLM-based recommendation assistants . |
| Approach: | They propose a reinforcement fine-tuning framework that enhances LLM-based recommendation . they use a dual-graph Enhanced Reward Shaping framework to integrate recommendation metrics . |
| Outcome: | The proposed framework outperforms state-of-the-art recommendations and preserves core abilities. |
Prior Prompt Engineering for Reinforcement Fine-Tuning (2025.emnlp-main)
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| Challenge: | Existing studies have focused on algorithms, reward shaping, and data curation, but prior prompt engineering is understudied. |
| Approach: | They investigate prior prompt engineering (pPE) in reinforcement fine-tuning . they translate five representative iPE strategies into corresponding pPE approaches . |
| Outcome: | The proposed approaches outperform iPE-prompted models on in-domain and out-of-domain benchmarks. |