3 Commits

Include renames

Author	SHA1	Message	Date
karti	61939676a5	Pin Arena environments to stable Verifiers	2026-08-25 10:33:35 -07:00
kartiandClaude Opus 5	122a858b61	pin verifiers 0.3.1.dev59, and make every config say subprocess ... The 600-second per-model-call ceiling that capped the thinking-on run at n=6 is gone: DEFAULT_TIMEOUT is read=None in all nine venvs. There is NO verifiers 0.4.0. The `version = "0.4.0"` in the vendored tree belongs to a [[tool.uv.dependency-metadata]] block for nemo-gym, and reading it as verifiers' own sends you hunting a release that does not exist and concluding the bump is blocked. The vendored checkout is v0.3.0-59-g4bcb48e5, published to PyPI as 0.3.1.dev59, and the installed wheel diffs clean against the whole vendored v1 tree. Pinned as an ordinary index dependency: a path source is reproducible only on this box and a git URL pins a commit no wheel matches. ⚠️ dev59 changes the default agent runtime from subprocess to `prime`, a REMOTE sandbox. Without an explicit runtime every eval here exits "not authenticated with prime" before a single rollout — having already created its output directory and a zero-line traces.jsonl, so it reads as a failed eval rather than an unauthenticated one. All nine configs now say subprocess. Nothing here needs a sandbox: the reward is a pure function of the trace and the harness is null. Output layout is flat now — <env>--<model>--<harness>--<hex8>/ with configs/, traces.jsonl and a logs/eval.log that 0.3.0 never wrote. `-o` is the output_dir, not the run dir; `--run.dir` pins the leaf. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>	2026-08-21 23:14:05 -07:00
kartiandClaude Opus 5	4147de0282	tera-crow-nav: the first spatial taskset, and the hold sweep that sized its budget ... The bridge becomes a runnable taskset. The crux is that these are continuous control environments and the agent emits text: the model issues a control decision that is held for N steps, and the hold length is measured rather than guessed. At one decision every 32 steps the scripted baseline returns -2.807 and reaches the waypoint 0% of the time, against 5.484 and 100% at every frame. Open loop — one decision for the whole flight — returns -3.764 against an inaction floor of -4.161. A bird that sets a course and leaves does no better than one that does nothing, which is why crow-nav carries the strongest rule-4 claim of the five spatial environments. The budget bites from both ends: 8 turns reach the goal 44% of the time, 12 reach 88%, 14 reach 100%. 32 episodes against Qwen3.8-27B: mean 0.4603, no malformed replies in 412, every episode replaying to an identical checksum, and 16 of 32 spending all 16 turns. The gate fires 43.75% of the time and separates the four scenarios cleanly — whatever is wrong with the four dead data-environment gates, it is not that binary gates cannot discriminate. The oracle comes from the worker's own op. Nothing recomputes in Python a number that came out of TypeScript, least of all the reward denominator. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>	2026-08-21 17:44:46 -07:00