SeeUPO: Sequence-Level Agentic-RL with Convergence Guarantees
#SeeUPO #LLM agents #Convergence guarantees #Reinforcement Learning #arXiv #Sequence-level RL #Policy optimization
📌 Key Takeaways
- Researchers have introduced SeeUPO to solve the lack of convergence guarantees in agentic reinforcement learning.
- The framework specifically targets the instability issues found in multi-turn interactions for LLM-based agents.
- SeeUPO utilizes sequence-level updates to ensure training leads to an optimal policy rather than fluctuating or failing.
- The research provides a systematic analysis of policy update mechanisms and their impact on training stability.
📖 Full Retelling
A team of AI researchers published a breakthrough paper on February 11, 2025, introducing 'SeeUPO,' a novel sequence-level reinforcement learning (RL) framework designed to provide verified convergence guarantees for Large Language Model (LLM)-based agents. The technical report, released via the arXiv preprint server, addresses growing concerns regarding the inherent instability of traditional RL algorithms when applied to complex, multi-turn agentic scenarios. By establishing a more rigorous mathematical foundation for policy updates, the researchers aim to prevent the common training failures and suboptimal performance that currently plague the development of sophisticated autonomous AI systems.
The paper highlights a critical gap in contemporary machine learning: while RL has become the primary method for fine-tuning LLMs, most existing backbone algorithms were not originally designed for the long-horizon, multi-step interactions typical of AI agents. In these environments, subtle errors in policy updates can accumulate, leading to a total lack of convergence. The researchers systematically analyzed various combinations of policy update mechanisms and advantage estimation techniques to determine why standard models often fail to reach an optimal state during the training process.
To resolve these issues, SeeUPO introduces a sequence-level approach that considers the entire trajectory of an agent's actions rather than isolated token-level rewards. This methodology ensures that the agent's learning path remains stable even as the complexity of the task increases. The introduction of convergence guarantees is particularly significant for industrial applications where reliability is paramount. By providing a theoretical safety net, the researchers believe SeeUPO will enable the creation of more robust agents capable of handling reasoning, coding, and decision-making tasks with unprecedented consistency.
🏷️ Themes
Artificial Intelligence, Machine Learning, Reinforcement Learning
📚 Related People & Topics
Reinforcement learning
Field of machine learning
In machine learning and optimal control, reinforcement learning (RL) is concerned with how an intelligent agent should take actions in a dynamic environment in order to maximize a reward signal. Reinforcement learning is one of the three basic machine learning paradigms, alongside supervised learnin...
🔗 Entity Intersection Graph
Connections for Reinforcement learning:
- 🌐 Large language model (10 shared articles)
- 🌐 Reasoning model (3 shared articles)
- 🌐 Natural language processing (2 shared articles)
- 🌐 Neural network (2 shared articles)
- 🌐 PPO (2 shared articles)
- 🌐 Autonomous system (2 shared articles)
- 👤 Do It (1 shared articles)
- 🌐 Markov decision process (1 shared articles)
- 👤 Knowledge Graph (1 shared articles)
- 🌐 Linear temporal logic (1 shared articles)
- 🌐 Automaton (1 shared articles)
- 🌐 Artificial intelligence (1 shared articles)
📄 Original Source Content
arXiv:2602.06554v1 Announce Type: new Abstract: Reinforcement learning (RL) has emerged as the predominant paradigm for training large language model (LLM)-based AI agents. However, existing backbone RL algorithms lack verified convergence guarantees in agentic scenarios, especially in multi-turn settings, which can lead to training instability and failure to converge to optimal policies. In this paper, we systematically analyze how different combinations of policy update mechanisms and advan