This repository contains the pretrained observation compressor released with
CoACT. The model is a merged Qwen3.5-4B checkpoint
trained from trajectories collected with Qwen3.5-35B-A3B.
CoACT compresses each new environment observation before it enters a coding agent's
trajectory. It is trained with reward-selected supervision that favors compact observations
while preserving the agent's next action.
Our cross-agent generalization experiments show that compressors trained from different
agentic models achieve similar performance when transferred across agents. When evaluated
with Deepseek-v4-Pro, the compressor trained from Qwen3.5-35B-A3B trajectories achieves
74.5% pass@1 with 0.863M total tokens per instance, close to 75.0% pass@1 and 0.868M total
tokens for the compressor trained from Deepseek-v4-Pro trajectories. These results suggest
that this checkpoint can be used across agentic models without separately training a
compressor for each one, while agent-specific training may still provide a small performance
advantage. We therefore release it as the default CoACT compressor for use across agentic
models.
For deployment and evaluation instructions, see the
CoACT repository.
