ByteDance releases Agent R
Agent R has Reflective Self Training
Paper: Agent-R: Training Language Model Agents to Reflect via Iterative Self-Training
Code: https://github.com/bytedance/Agent-R
Researchers from ByteDance are interested in developing AI agents with error correction in interactive environments.
Hmm..What’s the background?
Agent-R is built upon the foundation of Large Language Models (LLMs). It uses a Partially Observable Markov Decision Process (POMDP) to formulate tasks where agents make decisions based on partial observations. The main approaches in this domain involve diverse trajectory and path optimization for these tasks.
So what is proposed in the research paper?
Here are the main insights:
Agent-R leverages Monte Carlo Tree Search (MCTS) to explore action paths and generate diverse trajectories
Agent-R employs an iterative self-training process where agents learn from their own experiences and dynamically correct errors, thus improving their policy
The framework demonstrates its effectiveness across three diverse interactive environments: WebShop, ScienceWorld, and TextCraft
The agent evaluates each action within its self-generated bad trajectories to identify errors based on its current capabilities. By identifying the first error step, the agent truncates the bad trajectory at this point and splices it with the adjacent correct path
Agent-R significantly outperforms baseline methods, including advanced closed-source models and agents trained on expert trajectories
What’s next?
The paper suggests future directions for research that includes refining the role of self-correction as a critical function in agent-based systems. The authors note that the enhanced reflection ability of the actor model could better serve as a component to assist other models.
Agent R has Reflective Self Training
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