Online Reasoning Calibration: Test-Time Training Enables Generalizable Conformal LLM Reasoning
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , , , , , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2026
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866912996028055552 |
|---|---|
| author | Zhou, Cai Wang, Zekai Wu, Menghua Zhu, Qianyu Julie Shi, Flora C. Wang, Chenyu Wilson, Ashia Jaakkola, Tommi Bates, Stephen |
| author_facet | Zhou, Cai Wang, Zekai Wu, Menghua Zhu, Qianyu Julie Shi, Flora C. Wang, Chenyu Wilson, Ashia Jaakkola, Tommi Bates, Stephen |
| contents | While test-time scaling has enabled large language models to solve highly difficult tasks, state-of-the-art results come at exorbitant compute costs. These inefficiencies can be attributed to the miscalibration of post-trained language models, and the lack of calibration in popular sampling techniques. Here, we present Online Reasoning Calibration (ORCA), a framework for calibrating the sampling process that draws upon conformal prediction and test-time training. Specifically, we introduce a meta-learning procedure that updates the calibration module for each input. This allows us to provide valid confidence estimates under distributional shift, e.g. in thought patterns that occur across different stages of reasoning, or in prompt distributions between model development and deployment. ORCA not only provides theoretical guarantees on conformal risks, but also empirically shows higher efficiency and generalization across different reasoning tasks. At risk level $δ=0.1$, ORCA improves Qwen2.5-32B efficiency on in-distribution tasks with savings up to 47.5% with supervised labels and 40.7% with self-consistency labels. Under zero-shot out-of-domain settings, it improves MATH-500 savings from 24.8% of the static calibration baseline to 67.0% while maintaining a low empirical error rate, and the same trend holds across model families and downstream benchmarks. Our code is publicly available at https://github.com/wzekai99/ORCA. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_01170 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Online Reasoning Calibration: Test-Time Training Enables Generalizable Conformal LLM Reasoning Zhou, Cai Wang, Zekai Wu, Menghua Zhu, Qianyu Julie Shi, Flora C. Wang, Chenyu Wilson, Ashia Jaakkola, Tommi Bates, Stephen Machine Learning Artificial Intelligence Computation and Language Applications While test-time scaling has enabled large language models to solve highly difficult tasks, state-of-the-art results come at exorbitant compute costs. These inefficiencies can be attributed to the miscalibration of post-trained language models, and the lack of calibration in popular sampling techniques. Here, we present Online Reasoning Calibration (ORCA), a framework for calibrating the sampling process that draws upon conformal prediction and test-time training. Specifically, we introduce a meta-learning procedure that updates the calibration module for each input. This allows us to provide valid confidence estimates under distributional shift, e.g. in thought patterns that occur across different stages of reasoning, or in prompt distributions between model development and deployment. ORCA not only provides theoretical guarantees on conformal risks, but also empirically shows higher efficiency and generalization across different reasoning tasks. At risk level $δ=0.1$, ORCA improves Qwen2.5-32B efficiency on in-distribution tasks with savings up to 47.5% with supervised labels and 40.7% with self-consistency labels. Under zero-shot out-of-domain settings, it improves MATH-500 savings from 24.8% of the static calibration baseline to 67.0% while maintaining a low empirical error rate, and the same trend holds across model families and downstream benchmarks. Our code is publicly available at https://github.com/wzekai99/ORCA. |
| title | Online Reasoning Calibration: Test-Time Training Enables Generalizable Conformal LLM Reasoning |
| topic | Machine Learning Artificial Intelligence Computation and Language Applications |
| url | https://arxiv.org/abs/2604.01170 |