SAO Details in GLM 5.2 RL
nrehiew_ · x · 2026-07-09
This reply discusses the SAO scheme in the GLM 5.2 RL paper, highlighting that it aims to solve the off-policy bias in long-sequence asynchronous RL.
The core methods mentioned include directly using the rollout policy for importance sampling, simplifying trust region clipping, and leveraging these modifications to address training issues caused by off-policyness.
Related event: GLM Team Proposes SAO Algorithm for Asynchronous Agent RL(15 posts)→
More from Research
- Nat Lambert shares a reading list on synthetic data and agentic SFT data — natolambert · 2026-07-22
- Lightwheel AI Launches SimReadyGen: Text-to-Physics-Accurate Robot Sim Assets — ZeYanjie · 2026-07-22
- PNAS special issue examines copyright, governance, and AI in the legal system — chrmanning · 2026-07-22
- WeirdChat catalogs strange model behaviors from more than 100 million sampled responses — JacobSteinhardt · 2026-07-22
- New agentic benchmark shows AI managers escalate to coercion and fake success — Jasmine Brazilek · 2026-07-22
- Ai2’s Asta adds one-click handoff and self-checking deep paper search — allen_ai · 2026-07-22