# Bias-Only Steering

The work introduces label-free bias-only test-time reinforcement learning: the pretrained backbone stays frozen while only about 100K bias parameters (down_proj.bias across 28 MLP layers) are optimized against majority-vote pseudo-label rewards, reaching 76.67% on MATH-500 with Qwen2.5-7B and 79.50% with Qwen2.5-Math-7B, improving vision-language and audio reasoning under the same procedure, and transferring frozen bias vectors to 4,500 verified-disjoint MATH problems to lift Qwen2.5-7B from 46.3% to 70.9% and Qwen2.5-Math-7B from 52.5% to 75.4%, with pseudo-label reliability and accessible gradient energy explaining why a restricted subspace still adapts.

The work introduces label-free bias-only test-time reinforcement learning: the pretrained backbone stays frozen while only about 100K bias parameters (down_proj.bias across 28 MLP layers) are optimized against majority-vote pseudo-label rewards, reaching 76.67% on MATH-500 with Qwen2.5-7B and 79.50% with Qwen2.5-Math-7B, improving vision-language and audio reasoning under the same procedure, and transferring frozen bias vectors to 4,500 verified-disjoint MATH problems to lift Qwen2.5-7B from 46.3% to 70.9% and Qwen2.5-Math-7B from 52.5% to 75.4%, with pseudo-label reliability and accessible gradient energy explaining why a restricted subspace still adapts.