title: Learning Adaptive Parallel Reasoning with Language Models
summary: Scaling inference-time computation has substantially improved the reasoning
  capabilities of language models. However, existing methods have significant
  limitations: serialized chain-of-thought approaches generate overly long
  outputs, leading to increased latency and exhausted context windows, while
  parallel methods such as self-consistency suffer from insufficient
  coordination, resulting in redundant computations and limited performance
  gains. To address these shortcomings, we propose Adaptive Parallel Reasoning
  (APR), a novel reasoning framework that enables language models to orchestrate
  both serialized and parallel computations end-to-end. APR generalizes existing
  reasoning methods by enabling adaptive multi-threaded inference using spawn()
  and join() operations. A key innovation is our end-to-end reinforcement
  learning strategy, optimizing both parent and child inference threads to
  enhance task success rate without requiring predefined reasoning structures.
  Experiments on the Countdown reasoning task demonstrate significant benefits of
  APR: (1) higher performance within the same context window (83.4% vs. 60.0% at
  4k context); (2) superior scalability with increased computation (80.1% vs.
  66.6% at 20k total tokens); (3) improved accuracy at equivalent latency (75.2%
  vs. 57.3% at approximately 5,000ms). APR represents a step towards enabling
  language models to autonomously optimize their reasoning processes through
  adaptive allocation of computation.
paper_authors._id: 6808480c49c8f78b6a4e492f
paper_authors.name: Jiayi Pan
paper_authors.status: admin_assigned
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paper_authors.fullname: Jiayi Pan
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paper_authors.user: Jiayi-Pan
paper_authors._id: 6808480c49c8f78b6a4e4930
paper_authors.name: Xiuyu Li
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paper_authors.fullname: Xiuyu Li
paper_authors.type: user
paper_authors.user: xiuyul
paper_authors._id: 6808480c49c8f78b6a4e4931
paper_authors.name: Long Lian
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paper_authors.fullname: Long(Tony) Lian
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paper_authors.user: longlian
paper_authors._id: 6808480c49c8f78b6a4e4932
paper_authors.name: Charlie Snell
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paper_authors._id: 605d07fa8a3450814bada877
paper_authors.avatarUrl: /avatars/eafe986982057fbaba962b99d5543477.svg
paper_authors.fullname: Charlie Snell
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paper_authors.user: sea-snell
paper_authors._id: 6808480c49c8f78b6a4e4933
paper_authors.name: Yifei Zhou
paper_authors.status: admin_assigned
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paper_authors._id: 64fd0229e0dc35986bd3c0e5
paper_authors.avatarUrl: /avatars/94f5698f9104dad7288edb4460026fd8.svg
paper_authors.fullname: Yifei Zhou
paper_authors.type: user
paper_authors.user: yifeizhou
paper_authors._id: 6808480c49c8f78b6a4e4934
paper_authors.name: Adam Yala
paper_authors.status: claimed_verified
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paper_authors.fullname: Adam Yala
paper_authors.type: user
paper_authors.user: yala
paper_authors._id: 6808480c49c8f78b6a4e4935
paper_authors.name: Trevor Darrell
paper_authors.status: admin_assigned
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paper_authors._id: 64cbdf02f103036e23d1c7f3
paper_authors.avatarUrl: /avatars/496069463900dea20929b57381182d39.svg
paper_authors.fullname: Trevor Darrell
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paper_authors.user: trevordarrell
paper_authors._id: 6808480c49c8f78b6a4e4936
paper_authors.name: Kurt Keutzer
paper_authors.status: admin_assigned
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paper_authors.fullname: Kurt Keutzer
paper_authors.type: user
paper_authors.user: kurtkeutzer
paper_authors._id: 6808480c49c8f78b6a4e4937
paper_authors.name: Alane Suhr
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-04-23T13:47:19.192Z
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paper_authors.avatarUrl: /avatars/3a495283955ec9e06e1829c7eb2cd9a4.svg
paper_authors.fullname: Alane Suhr
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paper_authors.user: alsuhr
paper_title: Learning Adaptive Parallel Reasoning with Language Models
paper_summary: Scaling inference-time computation has substantially improved the reasoning
  capabilities of language models. However, existing methods have significant
  limitations: serialized chain-of-thought approaches generate overly long
  outputs, leading to increased latency and exhausted context windows, while
  parallel methods such as self-consistency suffer from insufficient
  coordination, resulting in redundant computations and limited performance
  gains. To address these shortcomings, we propose Adaptive Parallel Reasoning
  (APR), a novel reasoning framework that enables language models to orchestrate
  both serialized and parallel computations end-to-end. APR generalizes existing
  reasoning methods by enabling adaptive multi-threaded inference using spawn()
  and join() operations. A key innovation is our end-to-end reinforcement
  learning strategy, optimizing both parent and child inference threads to
  enhance task success rate without requiring predefined reasoning structures.
  Experiments on the Countdown reasoning task demonstrate significant benefits of
  APR: (1) higher performance within the same context window (83.4% vs. 60.0% at
  4k context); (2) superior scalability with increased computation (80.1% vs.
  66.6% at 20k total tokens); (3) improved accuracy at equivalent latency (75.2%
  vs. 57.3% at approximately 5,000ms). APR represents a step towards enabling
  language models to autonomously optimize their reasoning processes through
  adaptive allocation of computation.
paper_ai_summary: Adaptive Parallel Reasoning (APR) enhances language model performance by optimally combining serialized and parallel computations through adaptive multi-threading and reinforcement learning.
paper_ai_keywords: Adaptive Parallel Reasoning APR serialized parallel computations spawn() join() reinforcement learning Countdown reasoning task multi-threaded inference context window scalability computation allocation