Label-free steering: Compressing test-time reinforcement learning into bias-only subspaces
Test-time reinforcement learning (TTRL) enables models to improve their reasoning without relying on labeled training data, but existing approaches typically optimize a large fraction of the model parameters. This raises a natural question: can effective test-time adaptation emerge when both the reward signal and the optimization space are severely restricted? We answer this question with label-free bias-only TTRL, which uses majority-vote pseudolabels as rewards and optimizes only ~100K bias parameters while keeping the pretrained backbone frozen. On MATH-500, our approach reaches 76.67% accuracy with Qwen2.5-7B, slightly exceeding our own labeled bias-steering reproduction while optimiz...
| Authors | Naveen Vakada, Mingyuan Li, Shaoxiong Ji |
|---|---|
| Primary category | cs.LG |
| Published | 16 September 2026 |
| Last updated at source | 8 October 2026 |
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