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Learning Adaptive Parallel Reasoning with Language Models

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arxiv 2504.15466 v2 pith:AETZG2TX submitted 2025-04-21 cs.AI cs.CL

Jiayi Pan , Xiuyu Li , Long Lian , Charlie Snell , Yifei Zhou , Adam Yala , Trevor Darrell , Kurt Keutzer
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Alane Suhr
This is my paper · ORCID
classification cs.AIcs.CL
keywords reasoningadaptivelanguagemodelsparallelcomputationcontextmethods
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Cited by 11 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Hidden Language Consistency Phenomena in Reasoning LLMs

    cs.CL 2026-08 conditional novelty 6.0 of 10

    Reasoning models often stop using the requested language as problems get harder, and this language breakdown can make accuracy look better than it is.

  2. Test-time Scaling over Perception: Resolving the Grounding Paradox in Thinking with Images

    cs.CV 2026-04 unverdicted novelty 6.0 of 10

    TTSP samples and filters multiple zoom-in exploration traces and iteratively consolidates validated observations into an Evidence Ledger, improving fine-grained multimodal reasoning on V* Bench, HR-Bench, TreeBench, a...

  3. Parallel-R1: Towards Parallel Thinking via Reinforcement Learning

    cs.CL 2025-09 conditional novelty 6.0 of 10

    Parallel-R1 uses SFT cold-start on easy math plus GRPO on hard math to instill parallel thinking in Qwen3-4B, reporting 8.4% average accuracy gains and a 42.9% AIME25 gain from a parallel-exploration scaffold.

  4. ParaThinker: Native Parallel Thinking as a New Paradigm to Scale LLM Test-time Compute

    cs.CL 2025-08 conditional novelty 6.0 of 10

    ParaThinker trains LLMs for native parallel reasoning and reports 7 to 12 percent higher accuracy on math benchmarks over sequential thinking with modest latency overhead.

  5. Reflect, Retry, Reward: Self-Improving LLMs via Reinforcement Learning

    cs.CL 2025-05 conditional novelty 6.0 of 10

    A GRPO-based method that rewards only self-reflection tokens, not answer tokens, improves LLM accuracy on function calling and Countdown math tasks using only binary success/failure feedback.

  6. VeriThinker: Learning to Verify Makes Reasoning Model Efficient

    cs.LG 2025-05 conditional novelty 6.0 of 10

    VeriThinker shows that fine-tuning a reasoning model only on a solution-verification task reduces chain-of-thought length on MATH500 and AIME by 20-45% while preserving or slightly improving accuracy.

  7. Efficient Reasoning on the Edge

    cs.LG 2026-03 accept novelty 5.5 of 10

    LoRA adapters, budget-forced GRPO, dynamic switching, parallel verification and FPTQuant enable practical chain-of-thought reasoning on quantized Qwen2.5-7B for edge devices.

  8. ParVL: Parallel Scaling and Expandable Compute Allocation for Multimodal LLMs

    cs.CV 2026-08 conditional novelty 5.0 of 10

    ParVL scales MLLM computation by running multiple prefix-conditioned ViT and LLM branches over a shared backbone, improving average benchmark scores by 0.3 to 0.9 points and showing task-dependent vision-language allocation.

  9. Adaptive Termination for Multi-round Parallel Reasoning: An Universal Semantic Entropy-Guided Framework

    cs.CL 2025-07 conditional novelty 5.0 of 10

    A semantic entropy-guided stopping rule for multi-round parallel LLM reasoning improves accuracy while reducing inference steps on five benchmarks.

  10. Reasoning on a Budget: A Survey of Adaptive and Controllable Test-Time Compute in LLMs

    cs.AI 2025-07 conditional novelty 5.0 of 10

    The survey's L1/L2 taxonomy and benchmark show that current reasoning models waste compute on easy problems and underthink hard ones, motivating more adaptive inference.

  11. Scaling over Scaling: Exploring Test-Time Scaling Plateau in Large Reasoning Models

    cs.AI 2025-05 reject novelty 2.0 of 10

    A probabilistic saturation model for test-time scaling is proposed and fitted to reasoning benchmarks, but the plateau 'prediction' is computed from the same per-problem data used to measure it.

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