GAE Processing for Agent Trajectories
ziv_ravid · x · 2026-07-11
The author discusses how advantages are propagated in agent trajectories: model actions and environmental observations interleave. If standard GAE is applied directly, the advantage would propagate across many observation tokens that the model didn't actually "generate." To fix this, they connect the last token of an action directly to the first token of the next action, preventing learning signals from reaching places they shouldn't.
This is a typical discussion on agent training methods, focusing on how to process interaction trajectories more reasonably.
Related event: GLM Team Proposes SAO Algorithm for Asynchronous Agent RL(15 posts)→
More from Research
- New review: how prefrontal cortex dynamics turn world models into flexible behavior — AnnaCiaunica · 2026-10-27
- LIBERO-MAX: Robot Policies Drop 11–26 Points When the World Changes Mid-Task — drmapavone · 2026-10-27
- Kolab Benchmark Uses Pro Screen Recordings to Show AI Agents Overestimate Collaboration Quality — minnesotanlp · 2026-10-27
- One BBQ Example Pushes LLM Fairness Scores Past 90%, EMNLP Paper Questions Benchmarks — radamihalcea · 2026-10-27
- Real2Sem2Real turns one phone video into 2,000+ robot demos, matching 150 teleop demos — Scobleizer · 2026-10-27
- Understanding MuonH (Hyperball) through weight norm geometry of scale-invariant networks — jasondeanlee · 2026-10-27