🔬Paper: Train Long-Context LLMs on Agent Trajectories
TL;DR
A new arXiv paper, Agent Context Compiler (ACC), turns the long tool-call trajectories agents already produce into long-context training data. It targets the blind spot in standard agent fine-tuning, which masks tool responses and only learns turn-level tool selection.
A new arXiv paper, Agent Context Compiler (ACC), turns the long tool-call trajectories agents already produce into long-context training data. It targets the blind spot in standard agent fine-tuning, which masks tool responses and only learns turn-level tool selection.
Key Points
Submitted to arXiv on 21 May 2026 (2605.21850)
Reuses agent trajectories instead of costly long-document curation
Targets the SFT blind spot where masked tool outputs go unused
Aims to integrate evidence scattered across many agent turns
Why It Matters
Free long-context training data harvested from agent runs could cut one of the bigger costs in building long-horizon agents.
Quick Facts
Comments
Be the first to comment
Enjoyed this article?
Get it daily. 7am. Free. Reads in 5 minutes.
Join 3,483 builders reading daily.