CMU trains LLMs to write memories with RL for continual learning across task streams
aviral_kumar2 · x · 2026-11-13
CMU researchers (Qixin Xu, Yuxiao Qu, Ian Wu, Aviral Kumar) published "How to Train Memory Writing for Continual Learning", studying how to train LLMs to write effective memories from on-the-fly experience so that future tasks become easier — a form of continual learning at deployment time.
- Setup: a single model alternates between ACT (solving a task) and WRITE (writing a memory that conditions the next rollout), trained end-to-end with reinforcement learning.
- Motivation: common approaches — prompting an LLM to summarize/compact experience, like coding agents appending to MEMORY.md — rely on prompt instructions and can't reliably capture what will actually help upcoming tasks.
- The authors tease that the picture is "more nuanced than you think", with notable trade-offs in training memory writing.
Related event: CMU Trains LLMs to Write Memory via RL for Continual Learning(2 posts)→
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