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Hle Benchmark Evolver

Runs HLE-oriented benchmark reward ingestion and curriculum generation for capability-evolver. Use when the user asks to optimize Humanity's Last Exam score, ingest question-level benchmark results, prioritize easy-first queues, or request an immediate benchmark progress result.

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Install

openclaw skills install hle-benchmark-evolver

HLE Benchmark Evolver

This skill operationalizes HLE score-driven evolution for OpenClaw.

When to Use

  • User asks to improve HLE score (for example target >= 60%).
  • User provides question-level benchmark output and wants it converted to reward.
  • User wants easy-first curriculum queue and next-focus questions.
  • User asks for an immediate benchmark result snapshot.

Inputs

  • Benchmark report JSON path (--report=/abs/path/report.json)
  • Optional benchmark id (cais/hle default)

Workflow

  1. Validate the report JSON exists and is parseable.
  2. Ingest report into capability-evolver benchmark reward state.
  3. Generate curriculum signals:
    • benchmark_*
    • curriculum_stage:*
    • focus_subject:*
    • focus_modality:*
    • question_focus:*
  4. Return a compact result summary for this run.

Run

node skills/hle-benchmark-evolver/run_result.js --report=/absolute/path/hle_report.json

Full automatic loop (starts evolution cycle):

node skills/hle-benchmark-evolver/run_pipeline.js --report=/absolute/path/hle_report.json --cycles=1

If your evaluator can be called from shell, let pipeline generate the report each cycle:

node skills/hle-benchmark-evolver/run_pipeline.js \
  --report=/absolute/path/hle_report.json \
  --eval_cmd="python /path/to/eval_hle.py --out {{report}}" \
  --cycles=3 --interval_ms=2000

If no --report is provided, it defaults to:

skills/capability-evolver/assets/gep/hle_report.template.json

Output Contract

Always print JSON with these fields:

  • benchmark_id
  • run_id
  • accuracy
  • reward
  • trend
  • curriculum_stage
  • queue_size
  • focus_subjects
  • focus_modalities
  • next_questions

Notes

  • This skill handles reward/curriculum ingestion. It does not directly solve HLE questions.
  • run_pipeline.js links ingestion, evolve, and solidify into one executable loop.