Researchers propose successive scale racing to improve agentic harness evolution
A new arXiv paper introduces Successive Scale Racing (SSR), a framework that adaptively selects evidence combinations for agentic harness evolution. The authors observe that expanding the task pool can paradoxically hurt existing methods, a failure mode revealed in their preliminary study. SSR builds diverse evidence sets based on task quality and coverage, then races candidate harnesses using a…
Key points
- SSR adaptively searches evidence combos and races candidate harnesses under a small compute budget
- Pass rate improves from 64.3% to 68.3% as tasks grow 40 → 200, while baselines drop
- Validation executions are reduced by 60% compared with prior methods
Across three benchmarks and six configurations, SSR attains state‑of‑the‑art results. When the task pool expands from 40 to 200 tasks, the pass rate rises from 64.3% to 68.3%, while baseline approaches all decline. The racing mechanism also trims validation executions by 60%, offering a more efficient optimization process.
The work suggests that careful evidence selection and adaptive racing can counteract the degradation seen in larger task sets, pointing to a scalable path for improving AI agents that rely on evolving instruction and tool harnesses.
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