DDRL Framework Mitigates Spurious Signals in Test-Time Reinforcement Learning for Math Reasoning
A recent study published on arXiv (2604.21327) tackles the issue of spurious signal amplification in test-time reinforcement learning (TTRL) related to mathematical reasoning. Researchers discovered that responses exhibiting medium consistency create an ambiguity region, which is a significant contributor to reward noise. They noted that group-relative advantage estimation can exacerbate these misleading signals. To mitigate this, the team introduced the Debiased and Denoised test-time Reinforcement Learning (DDRL) framework. DDRL employs a frequency-based sampling method to filter out ambiguous samples while ensuring a balanced representation of positive and negative examples. It also utilizes a debiased advantage estimation with fixed advantages to eliminate bias in group-relative policy optimization and incorporates a consensus-based off-policy strategy. The study was released on April 27, 2026.
Key facts
- Test-time reinforcement learning adapts models at inference via pseudo-labeling.
- Medium-consistency responses form an ambiguity region causing reward noise.
- Spurious signals can be amplified through group-relative advantage estimation.
- DDRL framework proposed to mitigate spurious signals.
- DDRL uses frequency-based sampling to exclude ambiguous samples.
- Debiased advantage estimation with fixed advantages removes group-relative bias.
- DDRL incorporates consensus-based off-policy learning.
- Paper published on arXiv on April 27, 2026.
Entities
Institutions
- arXiv