v0.14.0

A minor bump: new config keys runner_config.stop_after and a model's
lifecycle, a task's grader / extractor may name a models: entry that
sieval run launches, sieval run gains exit code 6 for an engine lost
mid-run, and --dry-run reports new barriers, phases and launches
fields. No identity or on-disk format changes: meta.json identity stays
schema 4, RESULT_SCHEMA_VERSION stays 1, and 0.13.x result directories
resume.

Migrating from 0.13.x

• Exit code 6 (#246): sieval run exits 6 when an engine it launched exits or fails its health check for 180 s in a row mid-run. It is not a refusal and writes no SIEVAL_REFUSAL line; under -o json, data.outcome is engine_died. A scheduler that treated every non-zero exit alike now sees this code; --resume retries the failed samples.
• New error_reason values (#246): engine_died (in flight when the engine was lost; a resume retries it and spends a retry) and engine_died_unsent (never sent; retried without spending one). An older SiEval reads both as ordinary retriable failures.
• stop_after goes in the top-level runner_config (#247). It is session-wide: a task's own runner_config.stop_after is refused, and it needs record_each_stage: true (the default). The stopped run writes no report.json unless it stopped after report, and records runs[].stopped_after.
• New error_reason value barrier_iteration (#247): with max_iterations unset, a sample asking to iterate past a barrier on inference or postprocess fails with it; a resume without that barrier can retry it. A task with max_iterations above 1 under such a barrier is refused before the run.
• --dry-run needs_inference vs launches (#247): the top-level needs_inference says whether a model under test starts; a launched judge or extractor does not set it. The new launches lists every engine the run starts, judges included: a caller that read needs_inference to decide whether the run needs GPUs should read launches.
• Barriers and lifecycles are scheduling only: they enter no identity and never refuse a resume. runs[] gains stopped_after and serving.lifecycles, both additive.

Added

• Stage barriers and stop_after. runner_config.stop_after: <stage> (preprocess, inference, postprocess, feedback or report) ends the run once every sample of every task has finished that stage or failed; --resume continues from the next stage with the same identities as an uninterrupted run. The barriers are stop_after plus every lifecycle boundary, with no key of their own; at each, no sample starts the next stage until all have finished this one. --dry-run lists them under barriers, with what implied each. From Python: MultiTaskRunner(stop_after=, barriers=, on_barrier=) (#247, closes #245).
• Model lifecycles and launched judges. A model sieval run launches may set lifecycle: {from: S, until: T} (either bound optional) to run its engine only between those barriers and then free its GPUs. A task's grader or extractor may name a models: entry, which sieval run launches like the subject; a launched judge that states weights is compared by them instead of its name in the feedback identity. With the subject until: inference and the judge from: inference, the judge takes the subject's GPUs within one run. Models alive at the same time get disjoint GPU blocks (indices into an inherited CUDA_VISIBLE_DEVICES) and their own ports, and a phase that does not fit is refused before any engine starts. A model starts only when some sample still needs it, so a resume with inference complete starts only the judge. --dry-run reports phases and launches; runs[].serving.lifecycles records the lifecycles (#247, closes #245).

Fixed

• A run fails fast when an engine it launched dies. An engine lost mid-run (an SGLang out-of-memory exit) used to fail every later request one by one through its client retries until no sample was left. sieval run now polls each launched engine's process every second and its health every 10 s; on a loss, every stage still needing that engine fails at once and stages on other engines carry on. Each task still writes report.json, the engine is stopped (its leftover process group killed so no worker keeps the GPUs), no later serving group starts, and the run exits 6 naming the engine, its exit code or failing health check, the failures per task and its last log lines. An engine stopped on purpose, lost after its last sample, or not launched by SiEval (api_base, sieval infer start) is not a loss (#246, follow-up to #243).