Agent Learning via Early Experience
📰 ArXiv cs.AI
Learn how agents can improve through early experience, overcoming challenges in reinforcement learning and achieving better performance in complex tasks
Action Steps
- Apply reinforcement learning to early experience data to improve agent performance
- Configure agents to learn from experience in environments with limited or no rewards
- Test agents in complex, real-world tasks to evaluate their performance
- Compare the performance of agents trained with early experience to those trained with supervised learning
- Build agents that can learn and improve through their own experience, outperforming humans in certain tasks
Who Needs to Know This
AI researchers and engineers working on language agents and reinforcement learning can benefit from this research, as it provides insights into improving agent performance through early experience
Key Insight
💡 Agents can learn and improve through early experience, even in environments with limited or no rewards, by using reinforcement learning and configuring them to learn from experience
Share This
🤖 Agents can learn & improve through early experience! 💡 Overcome reinforcement learning challenges and achieve better performance in complex tasks #AI #ReinforcementLearning
Full Article
Title: Agent Learning via Early Experience
Abstract:
arXiv:2510.08558v3 Announce Type: replace Abstract: A long-term goal of language agents is to learn and improve through their own experience, ultimately outperforming humans in complex, real-world tasks. However, training agents from experience data with reinforcement learning remains difficult in many environments, which either lack verifiable rewards (e.g., websites) or require inefficient long-horizon rollouts (e.g., multi-turn tool use). As a result, most current agents rely on supervised fi
Abstract:
arXiv:2510.08558v3 Announce Type: replace Abstract: A long-term goal of language agents is to learn and improve through their own experience, ultimately outperforming humans in complex, real-world tasks. However, training agents from experience data with reinforcement learning remains difficult in many environments, which either lack verifiable rewards (e.g., websites) or require inefficient long-horizon rollouts (e.g., multi-turn tool use). As a result, most current agents rely on supervised fi
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