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S. Shaikh and I. Cruz, "’Alexa, Do You Know Anything?’ The Impact of an Intelligent Assistant on Team Interactions and Creative Performance Under Time Scarcity," Dec. 2019. Available: https://doi.org/10.48550/arXiv.1912.12914
1912
Earlier work this paper cites.
A. Newell, J. C. Shaw, and H. A. Simon, "Report on a General Problem-Solving Program," in IFIP Congress , 1959. Available: https://api.semanticscholar.org/CorpusID:35199622
1959
Earlier work this paper cites.
J. McCarthy, "Recursive Functions of Symbolic Expressions and Their Computation by Machine, Part I," Commun. ACM , vol. 3, no. 4, pp. 184–195, Apr. 1960. Available: https://doi.org/10.1145/367177.367199
1960
Earlier work this paper cites.
L. Ouyang, J. Wu, X. Jiang, D. Almeida, C. L. Wainwright, P. Mishkin, C. Zhang, S. Agarwal, K. Slama, A. Ray, J. Schulman, J. Hilton, F. Kelton, L. Miller, M. Simens, A. Askell, P. Welinder, P. Christiano, J. Leike, and R. Lowe, "Training Language Models to Follow Instructions with Human Feedback," in Proc. 36th Int. Conf. Neural Information Processing Systems (NeurIPS) , New Orleans, LA, USA, 2022, Art. no. 2011
2011
Earlier work this paper cites.
J. Schulman, F. Wolski, P. Dhariwal, A. Radford, and O. Klimov, "Proximal Policy Optimization Algorithms," ArXiv , vol. abs/1707.06347, 2017. Available: https://api.semanticscholar.org/CorpusID:28695052
Original
2017
Earlier work this paper cites.
A. Radford, J. Wu, R. Child, D. Luan, D. Amodei, and I. Sutskever, "Language Models are Unsupervised Multitask Learners," 2019. Available: https://api.semanticscholar.org/CorpusID:160025533
2019
Earlier work this paper cites.
T. Schick, J. Dwivedi-Yu, R. Dessí, R. Raileanu, M. Lomeli, E. Hambro, L. Zettlemoyer, N. Cancedda, and T. Scialom, "Toolformer: Language Models Can Teach Themselves to Use Tools," in Proc. 37th Int. Conf. Neural Information Processing Systems (NeurIPS) , New Orleans, LA, USA, 2023, Art. no. 2997
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2025

Advancing SLM Tool-Use Capability using Reinforcement Learning

Paprunia, Dhruvi, Kharidia, Vansh, Doshi, Pankti

Understand

In an era where tool-augmented AI agents are becoming increasingly vital, our findings highlight the ability of Group Relative Policy Optimization (GRPO) to empower SLMs, which are traditionally constrained in tool use.

The ability to use tools effectively has become a defining feature of Large Language Models (LLMs), allowing them to access external data and internal resources.
As AI agents grow more sophisticated, tool-use capabilities have become indispensable.
While LLMs have made significant progress in this area, Small Language Models (SLMs) still face challenges in accurately integrating tool use, especially in resource-constrained settings.

Built on

S. Shaikh and I. Cruz, "’Alexa, Do You Know Anything?’ The Impact of an Intelligent Assistant on Team Interactions and Creative Performance Under Time Scarcity," Dec. 2019. Available: https://doi.org/10.48550/arXiv.1912.12914
1912
Earlier work this paper cites.
A. Newell, J. C. Shaw, and H. A. Simon, "Report on a General Problem-Solving Program," in IFIP Congress , 1959. Available: https://api.semanticscholar.org/CorpusID:35199622
1959
Earlier work this paper cites.
J. McCarthy, "Recursive Functions of Symbolic Expressions and Their Computation by Machine, Part I," Commun. ACM , vol. 3, no. 4, pp. 184–195, Apr. 1960. Available: https://doi.org/10.1145/367177.367199
1960
Earlier work this paper cites.
L. Ouyang, J. Wu, X. Jiang, D. Almeida, C. L. Wainwright, P. Mishkin, C. Zhang, S. Agarwal, K. Slama, A. Ray, J. Schulman, J. Hilton, F. Kelton, L. Miller, M. Simens, A. Askell, P. Welinder, P. Christiano, J. Leike, and R. Lowe, "Training Language Models to Follow Instructions with Human Feedback," in Proc. 36th Int. Conf. Neural Information Processing Systems (NeurIPS) , New Orleans, LA, USA, 2022, Art. no. 2011
2011
Earlier work this paper cites.
J. Schulman, F. Wolski, P. Dhariwal, A. Radford, and O. Klimov, "Proximal Policy Optimization Algorithms," ArXiv , vol. abs/1707.06347, 2017. Available: https://api.semanticscholar.org/CorpusID:28695052
Original
2017
Earlier work this paper cites.
A. Radford, J. Wu, R. Child, D. Luan, D. Amodei, and I. Sutskever, "Language Models are Unsupervised Multitask Learners," 2019. Available: https://api.semanticscholar.org/CorpusID:160025533
2019
Earlier work this paper cites.
T. Schick, J. Dwivedi-Yu, R. Dessí, R. Raileanu, M. Lomeli, E. Hambro, L. Zettlemoyer, N. Cancedda, and T. Scialom, "Toolformer: Language Models Can Teach Themselves to Use Tools," in Proc. 37th Int. Conf. Neural Information Processing Systems (NeurIPS) , New Orleans, LA, USA, 2023, Art. no. 2997
2023
Earlier work this paper cites.

Similar

H. Touvron et al. , "LLaMA: Open and Efficient Foundation Language Models," Feb. 2023. Available: https://doi.org/10.48550/arXiv.2302.13971
2023
Cited alongside, same era.
Z. Lu et al. , "Small Language Models: Survey, Measurements, and Insights," ArXiv , vol. abs/2409.15790, 2024. Available: https://api.semanticscholar.org/CorpusID:272832447
Original
2024
Cited alongside, same era.
F. Wang et al. , "A Comprehensive Survey of Small Language Models in the Era of Large Language Models: Techniques, Enhancements, Applications, Collaboration with LLMs, and Trustworthiness," ArXiv , vol. abs/2411.03350, 2024. Available: https://api.semanticscholar.org/CorpusID:273850126
Original
2024
Cited alongside, same era.
C. V. Nguyen et al. , "A Survey of Small Language Models," ArXiv , vol. abs/2410.20011, 2024. Available: https://arxiv.org/abs/2410.20011
Original
2024
Cited alongside, same era.

Then

Y. Qin, S. Liang, Y. Ye, K. Zhu, L. Yan, Y. Lu, Y. Lin, X. Cong, X. Tang, B. Qian, S. Zhao, L. Hong, R. Tian, R. Xie, J. Zhou, M. Gerstein, D. Li, Z. Liu, and M. Sun, "ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIs," in Proc. 12th Int. Conf. Learning Representations (ICLR) , 2024. Available: https://openreview.net/forum?id=dHng2O0Jjr
2024
Later among the works it cites.
Z. Shao, P. Wang, Q. Zhu, R. Xu, J. Song, X. Bi, H. Zhang, M. Zhang, Y. K. Li, Y. Wu, and D. Guo, "DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models," arXiv preprint, 2024. Available: https://arxiv.org/abs/2402.03300
Original
2024
Later among the works it cites.
L. E. Erdogan et al. , "TinyAgent: Function Calling at the Edge," in Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: System Demonstrations , Miami, Florida, USA, Nov. 2024, pp. 80–88. Available: https://aclanthology.org/2024.emnlp-demo.9/
2024
Later among the works it cites.

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…

L. Wang, C. Ma, X. Feng et al. , "A Survey on Large Language Model Based Autonomous Agents," Front. Comput. Sci. , vol. 18, 2024. Available: https://doi.org/10.1007/s11704-024-40231-1

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L. Chen, P. Wu, K. Chitta, B. Jaeger, A. Geiger, and H. Li, "End-to-End Autonomous Driving: Challenges and Frontiers," IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 46, no. 12, pp. 10164–10185, Dec. 2024. Available: https://doi.org/10.1109/TPAMI.2024.1234567

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S. Kapoor, B. Stroebl, Z. S. Siegel, N. Nadgir, and A. Narayanan, "AI Agents That Matter," arXiv preprint, 2024. Available: https://arxiv.org/abs/2407.01502

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J. Zhang et al. , "xLAM: A Family of Large Action Models to Empower AI Agent Systems." Available: https://github.com/SalesforceAIResearch/xLAM

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DeepSeek-AI, D. Guo, D. Yang, H. Zhang, J. Song, R. Zhang, R. Xu, Q. Zhu, S. Ma, P. Wang, X. Bi, X. Zhang, X. Yu, Y. Wu, Z. F. Wu, Z. Gou, Z. Shao, Z. Li, Z. Gao, A. Liu, B. Xue, et al., "DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning," arXiv preprint, 2025. Available: https://arxiv.org/abs/2501.12948

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Closest in time.