(8/n) Add ELO/Battles datasets as Task - #92
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Define task-owned arena and scoring defaults while keeping the Bradley-Terry implementation behind a registered scorer.
Separate battle-backed datasets from instruction adapters and allow ELO tasks to load their pinned arena sources through the dataset registry.
Route ELO through the shared benchmark dispatcher using resolved task definitions, pinned arena sources, and task-selected scoring defaults.
judge_extra_kwargs already carries max_tokens from judge.model_kwargs(), so passing it explicitly made every calibrate_temperature run crash with 'got multiple values for keyword argument max_tokens'. Matches the main judge construction.
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Summary
This PR packages the ELO/arena benchmarks as declarative tasks, unifying them with the rest of the task system. ELO runs are now selected, validated, and configured the same way as every other benchmark instead of through special-cased code paths.
Previously, ELO was dispatched by matching a task-name prefix, arena datasets were downloaded through their own hardcoded path, and arena choices and dataset revisions lived in Python constants. Adding or changing an arena meant touching several unrelated files.
After this change, each arena is a task definition:
elo-lmarena,elo-lmarena-100k,elo-lmarena-140k, andelo-comparia. The task YAML declares the arena, its pinned data sources, and its scoring defaults; the runtime resolves the task once and routes it through the shared benchmark registry.What changed
eloprotocol (arena, judging, Bradley-Terry scoring defaults), validated like the pairwise and MT-Bench protocols.dataset_revisions.pynow live with the task that uses them.judgearena tasks list; ELO runtime settings (battle sampling, calibration, bootstraps) stay in the run config, while stable arena identity stays in the task YAML.