ARXIV:2604.21327 · LLM REASONING · SUBMITTED 24 APR · 20:28 UTC · FRESHNESS STALE

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Understanding and Mitigating Spurious Signal Amplification in Test-Time Reinforcement Learning for Math Reasoning

Yongcan Yu · Lingxiao He · Jian Liang · Kuangpu Guo · Meng Wang · Qianlong Xie · +2 at arXiv

A framework to mitigate spurious signals in test-time reinforcement learning for math reasoning in LLMs, improving accuracy and stability.

Ship in 2-4 weeks›Score7.0Evidence unverified

Opportunity summary

Pain A framework to mitigate spurious signals in test-time reinforcement learning for math reasoning in LLMs, improving accuracy and stability.

Evidence 0 refs | 4 sources | 67% coverage

Blocker Evidence unverified

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PROBLEM

A framework to mitigate spurious signals in test-time reinforcement learning for math reasoning in LLMs, improving accuracy and stability. Through an empirical study, we observe that responses with medium consistency form an ambiguity region…

METHOD

Full abstract

Test-time reinforcement learning (TTRL) always adapts models at inference time via pseudo-labeling, leaving it vulnerable to spurious optimization signals from label noise. Through an empirical study, we observe that responses with medium consistency form an ambiguity region and constitute the primary source of reward noise. Crucially, we find that such spurious signals can be even amplified through group-relative advantage estimation. Motivated by these findings, we propose a unified framework, Debiased and Denoised test-time Reinforcement Learning (DDRL), to mitigate spurious signals. Concretely, DDRL first applies a frequency-based sampling strategy to exclude ambiguous samples while maintaining a balanced set of positive and negative examples. It then adopts a debiased advantage estimation with fixed advantages, removing the bias introduced by group-relative policy optimization. Finally, DDRL incorporates a consensus-based off-policy refinement stage, which leverages the rejection-sampled dataset to enable efficient and stable model updates. Experiments on three large language models across multiple mathematical reasoning benchmarks demonstrate that DDRL consistently outperforms existing TTRL baselines. The code will soon be released at https://github.com/yuyongcan/DDRL.

RESULT

ScienceToStartup currently rates this 7.0/10 on the public viability pass. Finally, DDRL incorporates a consensus-based off-policy refinement stage, which leverages the rejection-sampled dataset to enable efficient and stable model updates. A public repository is…

WHY NOW

LLM Reasoning moved forward this cycle; last verified April 2026. Public score 7.0/10. Implementation evidence is present through a linked repository.

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Opportunity summary

Score7.0

PainA framework to mitigate spurious signals in test-time reinforcement learning for math reasoning in LLMs, improving accuracy and stability.

Evidence0 refs | 4 sources | 67% coverage

Blockerno shell-level blocker reported

Analysis summary

A framework to mitigate spurious signals in test-time reinforcement learning for math reasoning in LLMs, improving accuracy and stability.

VerifiedSource: PDF linkedVerifiedPaperPack: citation fields availablePartialProof: unverified proof status

Competitive landscape

A framework to mitigate spurious signals in test-time reinforcement learning for math reasoning in LLMs, improving accuracy and stability.

Segment

LLM Reasoning

Adoption evidence

Public code linked for build inspection

Commercial read

7.0/10 public viability

Direct

not classified

Adjacent

not classified

Substitute

not classified

Unknown

not classified

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Competitive landscape

A framework to mitigate spurious signals in test-time reinforcement learning for math reasoning in LLMs, improving accuracy and stability.

Segment

LLM Reasoning

Adoption evidence

Public code linked for build inspection

Commercial read

7.0/10 public viability

Direct

not classified

Adjacent

not classified

Substitute

not classified

Unknown

not classified

Understanding and Mitigating Spurious Signal Amplification in Test-Time Reinforcement Learning for Math Reasoning

Understanding and Mitigating Spurious Signal Amplification in Test-Time Reinforcement Learning for Math Reasoning

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