Fine-tune a search agent with multi-turn RL on Amazon SageMaker AI
In short
Fine-tuning a search agent with multi-turn reinforcement learning (MTRL) on Amazon SageMaker AI offers a practical approach to enhancing the reliability and speed of search agents powered by large language models (LLMs). The traditional methods of supervised fine-tuning (SFT) and single-turn reinforcement learning (RL) fall short in providing the necessary multi-turn behavior. Fine-tuning, on the other hand, leverages the speed and cost benefits of smaller models while maintaining reliability through direct traini…
Key points
- Fine-tuning a search agent with multi-turn reinforcement learning (MTRL) on Amazon SageMa…: Fine-tuning a search agent with multi-turn reinforcement learning (MTRL) on Amazon SageMaker AI offers a practical approach to enhancing the reliability and speed of search agents powered by large language models (LLMs).
- The traditional methods of supervised fine-tuning (SFT) and single-turn reinforcement lea…: The traditional methods of supervised fine-tuning (SFT) and single-turn reinforcement learning (RL) fall short in providing the necessary multi-turn behavior.
- Fine-tuning, on the other hand, leverages the speed and cost benefits of smaller models w…: Fine-tuning, on the other hand, leverages the speed and cost benefits of smaller models while maintaining reliability through direct training on the agent's environment.