ReasoningBank: Scaling Agent Self-Evolving with Reasoning Memory

Understand

With the growing adoption of large language model agents in persistent real-world roles, they naturally encounter continuous streams of tasks.

• A key limitation, however, is their failure to learn from the accumulated interaction history, forcing them to discard valuable insights and repeat past errors.
• We propose ReasoningBank, a novel memory framework that distills generalizable reasoning strategies from an agent's self-judged successful and failed experiences.
• At test time, an agent retrieves relevant memories from ReasoningBank to inform its interaction and then integrates new learnings back, enabling it to become more capable over time.