Handling Hard Boundaries in Off-Policy Training
ziv_ravid · x · 2026-07-11
This segment covers an off-policy training approach: they discard the old policy and use the logprobs of the "current policy / rollout engine" as the importance ratio. Any samples exceeding the two-sided hard boundaries are directly masked out in the gradient, rather than applying standard clipping.
The core idea is that in agent training, they use stricter filtering to control off-policy errors instead of relying on mild clipping.
Related event: GLM Team Proposes SAO Algorithm for Asynchronous Agent RL(15 posts)→
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