Really like that every rollout gets its own sandbox — isolation is the part most agent-training setups skimp on.
We run coding agents in production harnesses, and the failure mode we see most isn't wrong code — it's non-terminating turns: hand an agent an open-ended objective and it can think in a loop for hours (one of ours burned its full daily inference cap doing exactly that this morning; it's the failure class we built ThumbGate around).
Does the reward setup here penalize rollouts that never emit a final answer, or do you hard-cap steps in the env? Curious because we ended up putting runaway detection at the harness layer, outside the model — prompt-level bounds kept getting reasoned around.
thanks for the question @IgorGanapolsky ! this is actually just a small experiment but yes, we added a hard timeout at the harness level. additionally the reward only pays off if it actually solves, plus a small penalty for runaway tool loops
