Not All Errors Are Equal: Consequence-Aware Reasoning Compute Allocation
Quick Answer
The study introduces consequence-aware test-time compute allocation, improving compute efficiency by 22-33% over difficulty-aware methods.
Quick Take
By prioritizing tasks based on potential costs of errors, the approach enhances performance across 700 software-engineering tasks in Lite and Multi-SWE-bench mini, ensuring high-consequence tasks receive adequate resources.
Key Points
- Consequence-aware allocation reduces cost-weighted loss by 22-33% compared to difficulty-aware routing.
- The scheduler routes higher-consequence tasks to larger compute tiers under the same budget.
- The issue-only predictor accurately identifies high-consequence tasks without misclassification.
- Experiments cover 700 software-engineering tasks, revealing orthogonality of consequence and difficulty.
- Priority-aware variant achieves over 30% improvement while retaining 90% of oracle gains.
Paper Resources
Source Excerpt
From the original publisher, up to about 700 charactersarXiv:2606. 04402v1 Announce Type: new Abstract: Modern reasoning models can allocate different amounts of test-time computation, such as thinking tokens, model calls, or compute budget, to different tasks. Existing methods generally drive this allocation by predicted difficulty and spend more compute where it is expected to raise accuracy. This implicitly assumes that all failures cost the same, since an accuracy objective weights every task equally.
However, such an assumption does not hold in deployment: A typo in a log message and a migration that corrupts a production database both count as one benchmark failure, but their real-world costs are fundamentally different. …
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