CoBa: Cost-Effective Test-Time Scaling via Compute-Balanced Routing
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TL;DR - CoBa reframes test-time scaling as a compute-allocation problem, using a routing policy that decides whether the next unit of compute goes to generation, verification, or stopping. It matches strong scaling baselines at roughly half the token cost, which matters for local/budget-constrained reasoning systems.
- Frames sampling more solutions, longer chains of thought, and stronger evaluators as competing uses of a fixed inference budget rather than independent knobs.
- The policy generates a small candidate set, applies cheap verification broadly, then escalates only uncertain or high-value candidates to stronger verification.
- Across 3,129 example-generator evaluations (MATH-500, AIME 2024/2025, AMC 2023, procedural symbolic reasoning), CoBa-Routed-Strong hits 85.13% macro accuracy vs. 85.20% for a self-evaluation weighted-voting proxy using 49.1% fewer parameter-weighted tokens, and matches best-of-16 majority voting within 0.01 points with 58.9% fewer tokens.
- Paired bootstrap tests show significant gains over single-sample decoding and a small residual best-of-16 edge at much higher cost; the gap to a pool oracle indicates remaining headroom for better routing.
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CoBa: Cost-Effective Test-Time Scaling via Compute-Balanced Routing
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TL;DR - CoBa reframes test-time scaling as a compute-allocation problem, using a routing policy that decides whether the next unit of compute goes to generation, verification, or stopping. It matches strong scaling baselines at roughly half the token cost, which matters for local/budget-constrained reasoning systems.
- Frames sampling more solutions, longer chains of thought, and stronger evaluators as competing uses of a fixed inference budget rather than independent knobs.
- The policy generates a small candidate set, applies cheap verification broadly, then escalates only uncertain or high-value candidates to stronger verification.
- Across 3,129 example-generator evaluations (MATH-500, AIME 2024/2025, AMC 2023, procedural symbolic reasoning), CoBa-Routed-Strong hits 85.13% macro accuracy vs. 85.20% for a self-evaluation weighted-voting proxy using 49.1% fewer parameter-weighted tokens, and matches best-of-16 majority voting within 0.01 points with 58.9% fewer tokens.
- Paired bootstrap tests show significant gains over single-sample decoding and a small residual best-of-16 edge at much higher cost; the gap to a pool oracle indicates remaining headroom for better routing.