Both reward-0 runs died with 'Termination reason: max_steps' after 3-4
exchanges -- the runner passes its generic default (8) down, far too
few for greet->verify->find->policy->act->confirm. Official tau2 runs
use 40+; floor, don't cap.
Co-Authored-By: Claude <noreply@anthropic.com>
loguru restricted to WARNING+ (every orchestrator step dumped full
messages -- thousands of lines per bench). AssistantMessage.is_final_
chunk now False on tool-call turns (official adapter semantics: True
means the agent is FINISHED talking); always-True handed the turn
back to the user prematurely mid-action-sequence.
Co-Authored-By: Claude <noreply@anthropic.com>
The user simulator (GLM via our adapter) inlines its scenario reasoning
in content as '...instructions...</think>reply' -- passed through
unstripped, the AGENT receives the scenario's secret instructions
(task goal, disclosure strategy), inflating rewards. Both channels now
trimmed at the last </think>.
Co-Authored-By: Claude <noreply@anthropic.com>
Same UX as the docker image chain: try local sources first, then the
GitHub URL, and on total failure print the exact manual commands --
with a warning that PyPI's 'tau2' is an unrelated physics package.
Co-Authored-By: Claude <noreply@anthropic.com>
