Learning from Peers in Reasoning Models
Large Reasoning Models (LRMs) have the ability to self-correct even when they
make mistakes in their reasoning paths. However, our study reveals that when
the reasoning process starts with a short but poor beginning, it becomes
difficult for the model to recover. We refer to this phenomenon as the "Prefix
Dominance Trap". Inspired by psychological findings that peer interaction can
promote self-correction without negatively impacting already accurate
individuals, we propose **Learning from Peers** (LeaP) to address this
phenomenon. Specifically, every tokens, each reasoning path summarizes its
intermediate reasoning and shares it with others through a routing mechanism,
enabling paths to incorporate peer insights during inference. However, we
observe that smaller models sometimes fail to follow summarization and
reflection instructions effectively. To address this, we fine-tune them into
our **LeaP-T** model series. Experiments on AIME 2024, AIME 2025, AIMO 2025,
and GPQA Diamond show that LeaP provides substantial improvements. For
instance, QwQ-32B with LeaP achieves nearly 5 absolute points higher than the
baseline on average, and surpasses DeepSeek-R1-671B on three math benchmarks
with an average gain of 3.3 points. Notably, our fine-tuned LeaP-T-7B matches
the performance of DeepSeek-R1-Distill-Qwen-14B on AIME 2024. In-depth analysis
reveals LeaP's robust error correction by timely peer insights, showing strong
error tolerance and handling varied task difficulty. LeaP marks a milestone by
enabling LRMs to collaborate during reasoning. Our code, datasets, and models
are available at https://learning-from-peers.github.io/ .
6822a9b0e6328d29a9c721d6
Tongxu Luo
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2025-05-13T08:12:19.861Z
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Tongxu Luo
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Zeno-Luo
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Wenyu Du
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2025-05-13T08:30:44.940Z
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Wenyu Du
user
wydu
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Jiaxi Bi
claimed_verified
2025-05-13T08:12:14.622Z
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Jiaxi Bi
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Jiaxi0775
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Stephen Chung
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Zhengyang Tang
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2025-05-13T08:31:01.937Z
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Zhengyang Tang
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tangzhy
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Hao Yang
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Min Zhang
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Benyou Wang
admin_assigned
2025-05-13T08:31:21.723Z
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Wang
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Benyou
Learning from Peers in Reasoning Models
Large Reasoning Models (LRMs) have the ability to self-correct even when they
make mistakes in their reasoning paths. However, our study reveals that when
the reasoning process starts with a short but poor beginning, it becomes
difficult for the model to recover. We refer to this phenomenon as the "Prefix
Dominance Trap". Inspired by psychological findings that peer interaction can
promote self-correction without negatively impacting already accurate
individuals, we propose **Learning from Peers** (LeaP) to address this
phenomenon. Specifically, every tokens, each reasoning path summarizes its
intermediate reasoning and shares it with others through a routing mechanism,
enabling paths to incorporate peer insights during inference. However, we
observe that smaller models sometimes fail to follow summarization and
reflection instructions effectively. To address this, we fine-tune them into
our **LeaP-T** model series. Experiments on AIME 2024, AIME 2025, AIMO 2025,
and GPQA Diamond show that LeaP provides substantial improvements. For
instance, QwQ-32B with LeaP achieves nearly 5 absolute points higher than the
baseline on average, and surpasses DeepSeek-R1-671B on three math benchmarks
with an average gain of 3.3 points. Notably, our fine-tuned LeaP-T-7B matches
the performance of DeepSeek-R1-Distill-Qwen-14B on AIME 2024. In-depth analysis
reveals LeaP's robust error correction by timely peer insights, showing strong
error tolerance and handling varied task difficulty. LeaP marks a milestone by
enabling LRMs to collaborate during reasoning. Our code, datasets, and models
are available at https://learning-from-peers.github.io/ .
LeaP, a peer interaction mechanism for large reasoning models, improves error correction and performance across various math benchmarks by enabling collaborative reasoning paths.
Learning from Peers (LeaP) Prefix Dominance Trap peer interaction routing mechanism intermediate reasoning fine-tuning LeaP-T error correction QwQ-32B DeepSeek-R1-671B DeepSeek-R1-Distill-Qwen-14B AIME 2024 AIME 2025 AIMO 2025 GPQA Diamond