Engramlab: RL-trained weight updates match text-based memory with less data and lower latency

dan_fried · x · 2026-11-06

Engramlab's new blog investigates how RL complements text-based memory by teaching models how to use their notes. Key findings:

Weight updates can be as effective as context-based memory updates with similarly small amounts of data
Weight-adaptive agents have lower inference-time latency
Training can be cheaper than building notes and stays robust through millions of tokens of edits

The retweeter notes this aligns with their TAHI research. Figures show context-adaptive agents produce 143%-176% longer messages than baseline and weight-adaptive agents on writing and data-viz tasks.

Original post →

Engramlab: RL-trained weight updates match text-based memory with less data and lower latency

More from coding & agent