The authors fine-tuned Qwen3-4B and Qwen3-8B agent models on trajectories containing actions and subsequent environment observations.
Predicting environment observations during fine-tuning improves later agent exploration
ActObs trains models to predict action consequences already recorded in trajectories, producing stronger reinforcement-learning outcomes without additional data or computation.
Big Tech
Juzheng Zhang · Disha Makhija · Manoj Ghuhan Arivazhagan · Vinayshekhar Bannihatti Kumar · Rashmi Gangadharaiah
University of Maryland · AWS AI Labs
Research Digest··2 min read
Zhang et al.
Why this paper
From AWS AI Labs and University of Maryland · Part of Credit Assignment in Agentic RL, now 17 papers
In one line
Supervising agent language models to predict observation tokens during SFT, not just actions, improves downstream RL exploration and pass@k.
What we could check
- ·No code link found
- ·No weights link found
- ·No dataset link found
- ·No compute details found
- ·No stated limitations found
- ·No benchmark numbers found
Observed from the paper text and links we have. Absence here means we did not find it, not that it does not exist.
§