Clarification on Rollout Timeouts, Inference Concurrency, and Their Impact on SkillOpt Evaluation
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The payload names no specific adapter or file, only that several benchmark adapters set hard=0 and soft=0 on timed-out rollouts; search the benchmark adapters for that timeout handling first. Answering the five questions needs the authors' experiment setup and timeout logs, so this is not a self-contained change. Done means a maintainer reply or a documented timeout policy, and a newcomer should not start without that.
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Hi authors,
I noticed that several benchmark adapters assign hard=0 and soft=0 to timed-out rollouts.
Since inference latency depends heavily on GPU resources, serving configuration, and request concurrency, this may introduce infrastructure-dependent noise into both validation scores and optimization feedback.
Could you clarify:
- What GPU configuration and inference concurrency were used in the paper's experiments?
- What were the observed timeout rates across different benchmarks?
- Were infrastructure timeouts retried, excluded, or treated as task failures?
- Were timeout trajectories included in the reflection stage?
- Was the same inference infrastructure and timeout policy used for all baselines?
I'm particularly concerned that infrastructure-related timeouts may affect the validation gate and lead to incorrect skill updates.
Thanks!
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