How is the baseline evaluation score aggregated across runs? (single-seed vs multi-seed / best-of-N)

Hi, thanks for the great work. I'm trying to reproduce your reported baseline numbers with the qwen backend and want to confirm the exact eval protocol before I blame my setup.

Reading through engine/trainer.py, I see that the baseline is a single call:

engine/trainer.py:1001-1002

baseline_dir = os.path.join(out_root, "selection_eval_baseline")
baseline_results = adapter.rollout(sel_env, skill_init, baseline_dir)

And for livemathematicianbench, configs/livemathematicianbench/default.yaml sets max_turns: 1, so each item gets a single LLM call. qwen_backend.py also defaults to temperature=0.7, so per-run stochasticity is non-trivial.

A few questions:

1. Are the baseline numbers you report from a single seed / single run, or aggregated across N runs?
2. If aggregated, is it mean, median, or best-of-N?
3. Is there any code path (that I might have missed) where a skill is rolled out multiple times and the max/mean is taken?

Thanks!

Read engine/trainer.py around lines 1001-1002, configs/livemathematicianbench/default.yaml, and qwen_backend.py. Trace the baseline rollout path and check whether repeated runs or aggregation are implemented. Done means documenting whether the reported baseline uses one run or multiple runs and, if applicable, which aggregation method is used.