Where Do Test-Time Scaling and Training Fall Short in Individual Stance Prediction?
Test-time scaling and post-training have improved LLM performance in coding and mathematical reasoning, but their effectiveness for individual stance prediction remains unclear. We study this question by predicting a person's stance in a new discussion from their history. We evaluate widely used test-time scaling strategies and post-training methods, such as supervised fine-tuning and reinforcement learning, and identify four failure modes across generation, selection, and learning: (1) incorrect consensus, where repeated samples agree on the wrong stance; (2) selection failure, where generation covers the observed stance but selection misses it; (3) response overfitting, where supervised...
| Authors | Yuyang Zhao, Xuan Liu, HaoYang Shang et al. |
|---|---|
| Primary category | cs.CL |
| Published | 27 September 2026 |
| Last updated at source | 6 October 2026 |
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