The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
AgentTuning: Enabling Generalized Agent Abilities for LLMs
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“Frameworks like Toolformer (Schick et al., 2023) employed a self-supervised objective to teach models where to insert API calls, while others like FireAct (Chen et al., 2023), AgentTuning (Zeng et al., 2024), Agent-FLAN (Chen et al., 2024h) fine-tuned models on expert-generated or curated datasets of tool-interaction trajectories (e.g., AgentBank (Song et al., 2024a), APIBank (Li et al., 2023b)).”
From The landscape of agentic reinforcement learning for LLMs: a survey (2025) · cites this paper as Zeng et al., 2024
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Citations
Cites
(2023) · doi:10.52202/075280-1220 · Cited by 134
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Cited by
Unifying Large Language Models and Knowledge Graphs: A Roadmap
Pan, S., Luo, L., Wang, Y., Chen, C., Wang, J., & Wu, X. (2024) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2024.3352100 · Cited by 1,376
Agentic AI: Autonomous intelligence for complex goals–a comprehensive survey
Acharya, D. B., Kuppan, K., & Divya, B. (2025) · IEEE Access · doi:10.1109/access.2025.3532853 · Cited by 717
The landscape of agentic reinforcement learning for LLMs: a survey
Zhang, G., Geng, H., Yu, X., Yin, Z., Zhang, Z., Tan, Z., Zhou, H., Li, Z., Xue, X., Li, Y., et al. (2025) · arXiv · doi:10.48550/arxiv.2509.02547 · Cited by 229
MEM1: Learning to Synergize Memory and Reasoning for Efficient Long-Horizon Agents
Zhou, Z., Qu, A., Wu, Z., Kim, S., Prakash, A., Rus, D., Zhao, J., Low, B. K. H., & Liang, P. P. (2025) · arXiv · doi:10.48550/arxiv.2506.15841 · Cited by 218
CogAgent: A Visual Language Model for GUI Agents
Hong, W., Wang, W., Lv, Q., Xu, J., Yu, W., Ji, J., Wang, Y., Wang, Z., Dong, Y., Ding, M., et al. (2024) · 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52733.2024.01354 · Cited by 202
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Metadata
- Authors
- Zeng, A., Liu, M., Lu, R., Wang, B., Liu, X., Dong, Y., & Tang, J.
- Venue
- Findings of the Association for Computational Linguistics ACL 2024
- Published
- 2024
- Pages
- 3053-3077
- Cited by
- 40
- References
- 1
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- CoChat. (2026). Citation check: AgentTuning: Enabling Generalized Agent Abilities for LLMs. https://app.cochat.ai/papers/10.18653/v1/2024.findings-acl.181
- The paper
- Zeng, A., Liu, M., Lu, R., Wang, B., Liu, X., Dong, Y., & Tang, J. (2024). AgentTuning: Enabling Generalized Agent Abilities for LLMs. Findings of the Association for Computational Linguistics ACL 2024. https://doi.org/10.18653/v1/2024.findings-acl.181
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
