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RAGEN: Understanding Self-Evolution in LLM Agents via Multi-Turn Reinforcement Learning

Paper · 2025 · Zihan Wang, Kangrui Wang, Qineng Wang et al.

Summary

The paper asks how LLM agents can be trained with reinforcement learning to improve through their own experience in interactive settings. Unlike single-turn math or coding, these settings involve many turns and noisy environment responses. The authors define StarPO, a framework that optimizes whole agent trajectories (observations, thinking traces, actions, rewards) with either PPO or GRPO. They also release RAGEN, a modular open-source system covering rollout generation, reward assignment and policy updates. Experiments use four environments: Bandit, Sokoban, Frozen Lake and WebShop. They show that multi-turn agent RL often falls into an "Echo Trap". In this collapse, reward variance shrinks, entropy drops and gradients spike. A stabilized variant, StarPO-S, counters this by filtering trajectories, adding a critic baseline and decoupling clipping. Further analysis shows that rollout design and reward design strongly affect learning. When rewards reflect only the final outcome, explicit reasoning tends to fade or becomes hallucinated. The work matters because it offers both reusable infrastructure and practical design guidance for stable agent RL.

Key points

  • StarPO (State-Thinking-Actions-Reward Policy Optimization) treats a full multi-turn trajectory as the unit of optimization. In each turn the model writes <think>…</think><answer>…</answer> output, and the framework supports both critic-based PPO and critic-free GRPO.
  • The four test environments span different challenges:
    • Bandit: single-turn and stochastic.
    • Sokoban: multi-turn and deterministic.
    • Frozen Lake: multi-turn and stochastic.
    • WebShop: multi-turn and open-domain.
    • A semantically reversed BanditRev variant is also studied.
  • StarPO-S reduces Echo Trap instability through three changes: it keeps trajectories selected by how much they vary, uses a critic as a baseline, and applies clipping in a decoupled manner.
  • Rollout design helps training in three ways:
    • Start from diverse initial states and sample several responses for each.
    • Allow multiple actions per turn.
    • Refresh rollouts often. In the Online-k experiments (k = 1, 2, 5, 10), the lowest reuse, Online-1, converged better.
  • Reasoning in multi-turn tasks shrinks over training, and no-think variants often match or beat thinking ones. The authors suggest format penalties and finer-grained rewards as remedies. They list small-scale tasks, no replay buffers and no multimodal tasks as limitations.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Fields, topics and concepts

📚 Shelves 4The field of science this work belongs to (OpenAlex fields).Computer Science · 18,614 worksArtificial Intelligence · 17,965 worksArts and Humanities · 17,450 worksLanguage and Linguistics · 17,296 works

Cite

Cite as Zihan Wang, Kangrui Wang, Qineng Wang, Pingyue Zhang, Linjie Li, Zhengyuan Yang, Xing Jin, Kefan Yu, Minh Nhat Nguyen, Licheng Liu, Eli Gottlieb, Yiping Lu, Kyunghyun Cho, Jiajun Wu, Li Fei-Fei, Lijuan Wang, Yejin Choi and Manling Li. 2025. RAGEN: Understanding Self-Evolution in LLM Agents via Multi-Turn Reinforcement Learning. arXiv:2504.20073.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.48550/arxiv.2504.20073 · arXiv 2504.20073

@misc{wang2025ragen,
  title = {{RAGEN: Understanding Self-Evolution in LLM Agents via Multi-Turn Reinforcement Learning}},
  author = {Zihan Wang, Kangrui Wang, Qineng Wang, Pingyue Zhang, Linjie Li, Zhengyuan Yang, Xing Jin, Kefan Yu, Minh Nhat Nguyen, Licheng Liu, Eli Gottlieb, Yiping Lu, Kyunghyun Cho, Jiajun Wu, Li Fei-Fei, Lijuan Wang, Yejin Choi and Manling Li},
  year = {2025},
  doi = {10.48550/arxiv.2504.20073},
  eprint = {2504.20073},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2504.20073}
}

References

73 references, 38 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

  1. G. Brockman, V. Cheung, L. Pettersson, J. Schneider, J. Schulman, J. Tang, and W. Zaremba. Openai gym, 2016. URL https://arxiv.org/abs/1606.01540 . ↗
  2. L. Chen, K. Lu, A. Rajeswaran, K. Lee, A. Grover, M. Laskin, P. Abbeel, A. Srinivas, and I. Mordatch. Decision transformer: Reinforcement learning via sequence modeling. arXiv preprint arXiv:2106.01345, 2021. find
  3. W. Chen, Y. Su, J. Zuo, C. Yang, C. Yuan, C. Qian, C.-M. Chan, Y. Qin, Y. Lu, R. Xie, et al. Agentverse: Facilitating multi-agent collaboration and exploring emergent behaviors in agents. arXiv preprint arXiv:2308.10848, 2(4):6, 2023.
  4. P. Christiano, J. Leike, T. B. Brown, M. Martic, S. Legg, and D. Amodei. Deep reinforcement learning from human preferences, 2023. URL https://arxiv.org/abs/1706.03741 . ↗
  5. DeepSeek-AI. Deepseek LLM: scaling open-source language models with longtermism. CoRR, abs/2401.02954, 2024. URL https://doi.org/10.48550/arXiv.2401.02954 . ↗
  6. DeepSeek-AI, Q. Zhu, D. Guo, Z. Shao, D. Yang, P. Wang, R. Xu, Y. Wu, Y. Li, H. Gao, S. Ma, W. Zeng, X. Bi, Z. Gu, H. Xu, D. Dai, K. Dong, L. Zhang, Y. Piao, Z. Gou, Z. Xie, Z. Hao, B. Wang, J. Song, D. Chen, X. Xie, K. Guan, Y. You, A. Liu, Q. Du, W. Gao, X. Lu, Q. Chen, Y. Wang, C. Deng, J. Li, C.… — DeepSeek-Coder-V2: Breaking the Barrier of Closed-Source Models in Code Intelligence
  7. 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, B. Wang, B. Wu, B. Feng, C. Lu, C. Zhao, C. Deng, C. Zhang, C. Ruan, D. Dai, D. Chen, D. Ji, E. Li, F. Lin, F. Dai, F. Lu… — DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning
  8. P. Dell’Aversana. The frozen lake problem. an example of optimization policy, 12 2021. find
  9. Y. Du, S. Li, A. Torralba, J. B. Tenenbaum, and I. Mordatch. Improving factuality and reasoning in language models through multiagent debate. arXiv preprint arXiv:2305.14325, 2023.
  10. L. Feng, Z. Xue, T. Liu, and B. An. Group-in-group policy optimization for llm agent training, 2025. URL https://arxiv.org/abs/2505.10978 . ↗
  11. Z. Gao, W. Zhan, J. D. Chang, G. Swamy, K. Brantley, J. D. Lee, and W. Sun. Regressing the relative future: Efficient policy optimization for multi-turn rlhf, 2024. URL https://arxiv.org/abs/2410.04612 . — [arXiv:2410.04612](https://arxiv.org/abs/2410.04612) ↗
  12. S. Goyal, Z. Ji, A. S. Rawat, A. K. Menon, S. Kumar, and V. Nagarajan. Think before you speak: Training language models with pause tokens, 2024. URL https://arxiv.org/abs/2310 .02226 . ↗
  13. T. Haarnoja, A. Zhou, P. Abbeel, and S. Levine. Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor, 2018. URL https://arxiv.org/abs/ 1801.01290 . ↗
  14. S. Hao, Y. Gu, H. Ma, J. J. Hong, Z. Wang, D. Z. Wang, and Z. Hu. Reasoning with language model is planning with world model. arXiv preprint arXiv:2305.14992, 2023a. find
  15. S. Hao, T. Liu, Z. Wang, and Z. Hu. Toolkengpt: Augmenting frozen language models with massive tools via tool embeddings. Advances in neural information processing systems, 36: 45870–45894, 2023b.
  16. A. Havrilla, Y. Du, S. C. Raparthy, C. Nalmpantis, J. Dwivedi-Yu, M. Zhuravinskyi, E. Hambro, S. Sukhbaatar, and R. Raileanu. Teaching large language models to reason with reinforcement learning, 2024. URL https://arxiv.org/abs/2403.04642 . ↗
  17. C. He, B. Zou, X. Li, J. Chen, and H. M. Junliang Xing. Enhancing llm reasoning with multi-path collaborative reactive and reflection agents, 2025. URL https://arxiv.org/abs/2501.0 0430 . ↗
  18. D. Herel and T. Mikolov. Thinking tokens for language modeling, 2024. URL https://arxiv.org/abs/2405.08644 . — [arXiv:2405.08644](https://arxiv.org/abs/2405.08644) ↗
  19. E. J. Hu, Y. Shen, P. Wallis, Z. Allen-Zhu, Y. Li, S. Wang, L. Wang, and W. Chen. Lora: Low-rank adaptation of large language models, 2021. URL https://arxiv.org/abs/2106.09685 . ↗
  20. J. Hu, Y. Zhang, Q. Han, D. Jiang, X. Zhang, and H.-Y. Shum. Open-reasoner-zero: An open source approach to scaling up reinforcement learning on the base model, 2025a. URL https://arxiv.org/abs/2503.24290 . ↗
  21. L. Hu, M. Huo, Y. Zhang, H. Yu, E. P. Xing, I. Stoica, T. Rosing, H. Jin, and H. Zhang. lmgame-bench: How good are llms at playing games?, 2025b. URL https://arxiv.org/abs/2505 .15146 . ↗
  22. C. E. Jimenez, J. Yang, A. Wettig, S. Yao, K. Pei, O. Press, and K. Narasimhan. SWE-bench: Can language models resolve real-world github issues? arXiv preprint arXiv:2310.06770, 2024.
  23. B. Jin, J. Yoon, P. Kargupta, S. O. Arik, and J. Han. An empirical study on reinforcement learning for reasoning-search interleaved llm agents, 2025a. URL https://arxiv.org/abs/2505 .15117 . ↗
  24. B. Jin, H. Zeng, Z. Yue, J. Yoon, S. Arik, D. Wang, H. Zamani, and J. Han. Search-r1: Training llms to reason and leverage search engines with reinforcement learning, 2025b. URL https://arxiv.org/abs/2503.09516 . ↗
  25. A. Junghanns and J. Schaeffer. Sokoban: Enhancing general single-agent search methods using domain knowledge. Artificial Intelligence, 129(1):219–251, 2001. ISSN 0004-3702. doi: https://doi.org/10.1016/S0004-3702(01)00109-6. URL https://www.sciencedirect.co m/science/article/pii/S0004370201001096 . — [doi:10.1016/s0004-3702(01)00109-6](https://doi.org/10.1016/s0004-3702%2801%2900109-6) ↗
  26. W. Kool, H. van Hoof, and M. Welling. Buy 4 REINFORCE samples, get a baseline for free!, 2019. URL https://openreview.net/forum?id=r1lgTGL5DE . ↗
  27. A. Kumar, V. Zhuang, R. Agarwal, Y. Su, J. D. Co-Reyes, A. Singh, K. Baumli, S. Iqbal, C. Bishop, R. Roelofs, L. M. Zhang, K. McKinney, D. Shrivastava, C. Paduraru, G. Tucker, D. Precup, F. Behbahani, and A. Faust. Training language models to self-correct via reinforcement learning, 2024. URL https:…
  28. G. Li, H. Hammoud, H. Itani, D. Khizbullin, and B. Ghanem. Camel: Communicative agents for" mind" exploration of large language model society. Advances in Neural Information Processing Systems, 36:51991–52008, 2023. — CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Society
  29. M. Li, S. Zhao, Q. Wang, K. Wang, Y. Zhou, S. Srivastava, C. Gokmen, T. Lee, L. E. Li, R. Zhang, W. Liu, P. Liang, L. Fei-Fei, J. Mao, and J. Wu. Embodied agent interface: Benchmarking llms for embodied decision making, 2025. URL https://arxiv.org/abs/2410.07166 . ↗
  30. H. Lightman, V. Kosaraju, Y. Burda, H. Edwards, B. Baker, T. Lee, J. Leike, J. Schulman, I. Sutskever, and K. Cobbe. Let’s verify step by step. arXiv preprint arXiv:2305.20050, 2023.
  31. B. Y. Lin, Y. Fu, K. Yang, F. Brahman, S. Huang, C. Bhagavatula, P. Ammanabrolu, Y. Choi, and X. Ren. Swiftsage: A generative agent with fast and slow thinking for complex interactive tasks. Advances in Neural Information Processing Systems, 36, 2024a.
  32. J. Lin, H. Zhao, A. Zhang, Y. Wu, H. Ping, and Q. Chen. Agentsims: An open-source sandbox for large language model evaluation, 2023. URL https://arxiv.org/abs/2308.04026 . — [arXiv:2308.04026](https://arxiv.org/abs/2308.04026) ↗
  33. J. Lin, H. Gao, X. Feng, R. Xu, C. Wang, M. Zhang, L. Guo, and S. Xu. Advances in embodied navigation using large language models: A survey, 2024b. URL https://arxiv.org/abs/ 2311.00530 . ↗
  34. B. Liu, Y. Jiang, X. Zhang, Q. Liu, S. Zhang, J. Biswas, and P. Stone. Llm+ p: Empowering large language models with optimal planning proficiency. arXiv preprint arXiv:2304.11477, 2023.
  35. Z. Liu, H. Hu, S. Zhang, H. Guo, S. Ke, B. Liu, and Z. Wang. Reason for future, act for now: A principled framework for autonomous llm agents with provable sample efficiency, 2024. URL https://arxiv.org/abs/2309.17382 . ↗
  36. Z. Liu, C. Chen, W. Li, P. Qi, C. D. Tianyu Pang, W. S. Lee, and M. Lin. Understanding r1-zero-like training: A critical perspective, 2025. URL https://arxiv.org/abs/2503.20783 . ↗
  37. M. Nguyen, A. Baker, C. Neo, A. Roush, A. Kirsch, and R. Shwartz-Ziv. Turning up the heat: Min-p sampling for creative and coherent llm outputs, 2024. URL https://arxiv.org/ab s/2407.01082 . ↗
  38. OpenAI. Introducing ChatGPT o1, 2024. URL https://openai.com/o1/ . Accessed: 2025-02-15. ↗
  39. 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, 20…
  40. J. Pan, J. Zhang, X. Wang, L. Yuan, H. Peng, and A. Suhr. Tinyzero. https://github.com/Jiayi-Pan/TinyZero, 2025. Accessed: 2025-01-24. ↗
  41. J. S. Park, J. C. O’Brien, C. J. Cai, M. R. Morris, P. Liang, and M. S. Bernstein. Generative agents: Interactive simulacra of human behavior, 2023. URL https://arxiv.org/abs/2304.034 42 . ↗
  42. J. Pfau, W. Merrill, and S. R. Bowman. Let’s think dot by dot: Hidden computation in transformer language models, 2024. URL https://arxiv.org/abs/2404.15758 . ↗
  43. Z. Qi, X. Liu, I. L. Iong, H. Lai, X. Sun, W. Zhao, Y. Yang, X. Yang, J. Sun, S. Yao, T. Zhang, W. Xu, J. Tang, and Y. Dong. Webrl: Training llm web agents via self-evolving online curriculum reinforcement learning, 2025. URL https://arxiv.org/abs/2411.02337 . — [arXiv:2411.02337](https://arxiv.org/abs/2411.02337) ↗
  44. Y. Qin, Y. Ye, J. Fang, H. Wang, S. Liang, S. Tian, J. Zhang, J. Li, Y. Li, S. Huang, W. Zhong, K. Li, J. Yang, Y. Miao, W. Lin, L. Liu, X. Jiang, Q. Ma, J. Li, X. Xiao, K. Cai, C. Li, Y. Zheng, C. Jin, C. Li, X. Zhou, M. Wang, H. Chen, Z. Li, H. Yang, H. Liu, F. Lin, T. Peng, X. Liu, and G. Shi. Ui… — UI-TARS: Pioneering Automated GUI Interaction with Native Agents
  45. J. Schulman, F. Wolski, P. Dhariwal, A. Radford, and O. Klimov. Proximal policy optimization algorithms, 2017. URL https://arxiv.org/abs/1707.06347 . ↗
  46. J. Schulman, P. Moritz, S. Levine, M. Jordan, and P. Abbeel. High-dimensional continuous control using generalized advantage estimation, 2018. URL https://arxiv.org/abs/15 06.02438 . ↗
  47. B. Settles. Active learning literature survey. 2009.
  48. 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, 2024a. URL https://arxiv.org/abs/2402.03300 . ↗
  49. Z. Shao, P. Wang, Q. Zhu, R. Xu, J. Song, M. Zhang, Y. Li, Y. Wu, and D. Guo. Deepseekmath: Pushing the limits of mathematical reasoning in open language models. arXiv preprint arXiv:2402.03300, 2024b.
  50. Y. Shen, K. Song, X. Tan, D. Li, W. Lu, and Y. Zhuang. Hugginggpt: Solving ai tasks with chatgpt and its friends in hugging face. Advances in Neural Information Processing Systems, 36, 2024.
  51. N. Shinn, F. Cassano, A. Gopinath, K. Narasimhan, and S. Yao. Reflexion: Language agents with verbal reinforcement learning. Advances in Neural Information Processing Systems, 36, 2024.
  52. I. Shumailov, Z. Shumaylov, Y. Zhao, N. Papernot, R. J. Anderson, and Y. Gal. Ai models collapse when trained on recursively generated data. Nat., 631(8022):755–759, July 2024. URL https://doi.org/10.1038/s41586-024-07566-y . ↗
  53. Y. Song, D. Yin, X. Yue, J. Huang, S. Li, and B. Y. Lin. Trial and error: Exploration-based trajectory optimization for llm agents, 2024. find
  54. C. J. Wang, D. Lee, C. Menghini, J. Mols, J. Doughty, A. Khoja, J. Lynch, S. Hendryx, S. Yue, and D. Hendrycks. Enigmaeval: A benchmark of long multimodal reasoning challenges, 2025. URL https://arxiv.org/abs/2502.08859 . — [arXiv:2502.08859](https://arxiv.org/abs/2502.08859) ↗
  55. K. Wang*, P. Zhang*, Z. Wang*, Q. Wang*, Y. Gao*, L. Li*, Z. Yang, C. Wan, H. Chen, Y. Lu, and M. Li. Vagen: Training vlm agents with multi-turn reinforcement learning, 2025. URL https://github.com/RAGEN-AI/VAGEN . ↗
  56. Q. Wang, Z. Wang, Y. Su, H. Tong, and Y. Song. Rethinking the bounds of llm reasoning: Are multi-agent discussions the key? arXiv preprint arXiv:2402.18272, 2024a.
  57. X. Wang, Z. Wang, J. Liu, Y. Chen, L. Yuan, H. Peng, and H. Ji. Mint: Evaluating llms in multi-turn interaction with tools and language feedback, 2024b. URL https://arxiv.org/ abs/2309.10691 . ↗
  58. Z. Wei, W. Yao, Y. Liu, W. Zhang, Q. Lu, L. Qiu, C. Yu, P. Xu, C. Zhang, B. Yin, H. Yun, and L. Li. Webagent-r1: Training web agents via end-to-end multi-turn reinforcement learning, 2025. URL https://arxiv.org/abs/2505.16421 . ↗
  59. Q. Wu, G. Bansal, J. Zhang, Y. Wu, S. Zhang, E. Zhu, B. Li, L. Jiang, X. Zhang, and C. Wang. Autogen: Enabling next-gen llm applications via multi-agent conversation framework. arXiv preprint arXiv:2308.08155, 2023.
  60. J. Xi, Y. He, J. Yang, Y. Dai, and J. Chai. Teaching embodied reinforcement learning agents: Informativeness and diversity of language use, 2024. URL https://arxiv.org/abs/2410 .24218 . ↗
  61. T. Xie, F. Zhou, Z. Cheng, P. Shi, L. Weng, Y. Liu, T. J. Hua, J. Zhao, Q. Liu, C. Liu, et al. Openagents: An open platform for language agents in the wild. arXiv preprint arXiv:2310.10634, 2023.
  62. B. Xu, Z. Peng, B. Lei, S. Mukherjee, Y. Liu, and D. Xu. Rewoo: Decoupling reasoning from observations for efficient augmented language models. arXiv preprint arXiv:2305.18323, 2023.
  63. A. Yang, B. Yang, B. Zhang, B. Hui, B. Zheng, B. Yu, C. Li, D. Liu, F. Huang, H. Wei, H. Lin, J. Yang, J. Tu, J. Zhang, J. Yang, J. Yang, J. Zhou, J. Lin, K. Dang, K. Lu, K. Bao, K. Yang, L. Yu, M. Li, M. Xue, P. Zhang, Q. Zhu, R. Men, R. Lin, T. Li, T. Xia, X. Ren, X. Ren, Y. Fan, Y. Su, Y. Zhang,… find
  64. S. Yao, H. Chen, J. Yang, and K. Narasimhan. Webshop: Towards scalable real-world web interaction with grounded language agents. Advances in Neural Information Processing Systems, 35:20744–20757, 2022a.
  65. S. Yao, J. Zhao, D. Yu, N. Du, I. Shafran, K. Narasimhan, and Y. Cao. React: Synergizing reasoning and acting in language models. arXiv preprint arXiv:2210.03629, 2022b.
  66. Q. Yu, Z. Zhang, R. Zhu, Y. Yuan, X. Zuo, Y. Yue, T. Fan, G. Liu, L. Liu, X. Liu, H. Lin, Z. Lin, B. Ma, G. Sheng, Y. Tong, C. Zhang, M. Zhang, W. Zhang, H. Zhu, J. Zhu, J. Chen, J. Chen, C. Wang, H. Yu, W. Dai, Y. Song, X. Wei, H. Zhou, J. Liu, W.-Y. Ma, Y.-Q. Zhang, L. Yan, M. Qiao, Y. Wu, and M.… — DAPO: An Open-Source LLM Reinforcement Learning System at Scale
  67. E. Zelikman, Y. Wu, J. Mu, and N. D. Goodman. Star: Bootstrapping reasoning with reasoning, 2022. URL https://arxiv.org/abs/2203.14465 . ↗
  68. S. Zeng, Q. Wei, W. Brown, O. Frunza, Y. Nevmyvaka, and M. Hong. Reinforcing multi-turn reasoning in llm agents via turn-level credit assignment, 2025a. URL https://arxiv.org/ abs/2505.11821 . — [arXiv:2505.11821](https://arxiv.org/abs/2505.11821) ↗
  69. W. Zeng, Y. Huang, Q. Liu, W. Liu, K. He, Z. Ma, and J. He. Simplerl-zoo: Investigating and taming zero reinforcement learning for open base models in the wild, 2025b. URL https://arxiv.org/abs/2503.18892 . ↗
  70. W. Zeng, Y. Huang, W. Liu, K. He, Q. Liu, Z. Ma, and J. He. 7b model and 8k examples: Emerging reasoning with reinforcement learning is both effective and efficient. https://hkust-nlp.notion.site/simplerl-reason , 2025c. Notion Blog. ↗
  71. Z. Zhang, C. Zheng, Y. Wu, B. Zhang, R. Lin, B. Yu, D. Liu, J. Zhou, and J. Lin. The lessons of developing process reward models in mathematical reasoning, 2025. URL https://arxiv.org/abs/2501.07301 . ↗
  72. Y. Zhou, A. Zanette, J. Pan, S. Levine, and A. Kumar. Archer: Training language model agents via hierarchical multi-turn rl, 2024. URL https://arxiv.org/abs/2402.19446 . ↗
  73. Y. Zhuang, X. Chen, T. Yu, S. Mitra, V. Bursztyn, R. A. Rossi, S. Sarkhel, and C. Zhang. Toolchain*: Efficient action space navigation in large language models with a* search. arXiv preprint arXiv:2310.13227, 2023. — [arXiv:2310.13227](https://arxiv.org/abs/2310.13227) find

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