適応型並列推論：効率的な推論スケーリングの新たなパラダイム

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*Overview of adaptive parallel reasoning.*

What if a reasoning model could decide *for itself* when to decompose and parallelize independent subtasks, how many concurrent threads to spawn, and how to coordinate them based on the problem at hand? We provide a detailed analysis of recent progress in the field of parallel reasoning, especially Adaptive Parallel Reasoning.

Disclosure: this post is part landscape survey, part perspective on adaptive parallel reasoning. One of the authors (Tony Lian) co-led ThreadWeaver (Lian et al., 2025), one of the methods discussed below. The authors aim to present each approach on its own terms.

Motivation

Recent progress in LLM reasoning capabilities has been largely driven by inference-time scaling, in addition to data and parameter scaling (OpenAI et al., 2024; DeepSeek-AI et al., 2025). Models that explicitly output reasoning tokens (through intermediate steps, backtracking, and exploration) now dominate math, coding, and agentic benchmarks. These behaviors allow models to explore alternative hypotheses, correct earlier mistakes, and synthesize conclusions rather than committing to a single solution (Wen et al., 2025).

The problem is that sequential reasoning scales linearly with the amount of exploration. Scaling sequential reasoning tokens comes at a cost, as models risk exceeding effective context limits (Hsieh et al., 2024). The accumulation of intermediate exploration paths makes it challenging for the model to disambiguate amongst distractors when attending to information in its context, leading to a degradation of model performance, also known as context-rot** (Hong, Troynikov and Huber, 2025). Latency also grows proportionally with reasoning length. For complex tasks requiring millions of tokens for exploration and planning, it’s not uncommon to see users wait tens of minutes or even hours for an answer (Qu et al., 2025). As we continue to scale along the output sequence length dimension, we also make inference slower, less reliable, and more compute-intensive. Parallel reasoning has emerged as a natural solution. Instead of exploring paths sequentially (Gandhi et al., 2024) and accumulating the context window at every step, we can allow models to explore multiple threads independently (threads don’t rely on each other’s context) and concurrently (threads can be executed at the same time).

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*Figure 1: Sequential vs. Parallel Reasoning*

Over recent years, a growing body of work has explored this idea across synthetic settings (e.g., the Countdown game (Katz, Kokel and Sreedharan, 2025)), real-world math problems, and general reasoning tasks.

From Fixed Parallelism to Adaptive Control

Existing approaches show that parallel reasoning can help, but most of them still decide the parallel structure outside the model rather than letting the model choose it.

Simple fork-and-join.**

• Self-consistency/Majority Voting — independently sample multiple complete reasoning traces, extract final answer from each, and return the most common one (Wang et al., 2023).

• Best-of-N (BoN) — similar to self-consistency, but uses a trained verifier to select the best solution instead of using majority voting (Stiennon et al., 2022).

• Although simple to implement, these methods often incur redundant computation across branches since trajectories are sampled independently.

Heuristic-based structured search.

• Tree / Graph / Skeleton of Thoughts — a family of structured decomposition methods that explores multiple alternative “thoughts” using known search algorithms (BFS/DFS) and prunes via LLM-based evaluation (Yao et al., 2023; Besta et al., 2024; Ning et al., 2024).

• Monte-Carlo Tree Search (MCTS) — estimates node values by sampling random rollouts and expands the search tree with Upper Confidence Bound (UCB) style exploration-exploitation (Xie et al., 2024; Zhang et al., 2024).

• These methods improve upon simple fork-and-join by decomposing tasks into non-overlapping subtasks; however, they require prior knowledge about the decomposition strategy, which is not always known.

Recent variants.

• ParaThinker — trains a model to run in two fixed stages: first generating multiple reasoning threads in parallel, then synthesizing them. They introduce trainable control tokens () and thought-specific positional embeddings to enforce independence during reasoning and controlled integration during summarization via a two-phase attention mask (Wen et al., 2025).

• GroupThink — multiple parallel reasoning threads can see each other’s partial progress at token level and adapt mid-generation. Unlike prior concurrent methods that operate on independent requests, GroupThink runs a single LLM producing multiple interdependent reasoning trajectories simultaneously (Hsu et al., 2025).

• Hogwild! Inference — multiple parallel reasoning threads share KV cache and decide how to decompose tasks without an explicit coordination protocol. Workers generate concurrently into a shared attention cache using RoPE to stitch together individual KV blocks in different orders without recomputation (Rodionov et al., 2025).

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*Figure 2: Various Strategies for Parallel Reasoning*

The methods above share a common limitation: the decision to parallelize, the level of parallelization, and the search strategy are imposed on the model, regardless of whether the problem actually benefits from it. However, different problems need different levels of parallelization, and that is something critical to the effectiveness of parallelization. For example, a framework that applies the same parallel structure to “What’s 25+42?” and “What’s the smallest planar region in which you can continuously rotate a unit-length line segment by 180°?” is wasting compute on the former and probably using the wrong decomposition strategy for the latter. In the approaches described above, the model is not taught this adaptive behavior. A natural question arises: What if the model could decide for itself when to parallelize, how many threads to spawn, and how to coordinate them based on the problem at hand?**

Adaptive Parallel Reasoning (APR) answers this question by making parallelization part of the model’s generated control flow. Formally defined, adaptivity refers to the model’s ability to dynamically allocate compute between parallel and serial operations at inference time. In other words, a model with adaptive parallel reasoning (APR) capability is taught to coordinate its control flow — when to generate sequences sequentially vs. in parallel.

It’s important to note that the concept of adaptive parallel reasoning was introduced by the work *Learning Adaptive Parallel Reasoning with Language Models* (Pan et al., 2025), but is a paradigm rather than a specific method. Throughout this post, APR refers to the paradigm, while “the APR method” denotes the specific instantiation from Pan et al. (2025).

This shift matters for three reasons. Compared to Tree-of-Thoughts, APR doesn’t need domain-specific heuristics for decomposition. During RL, the model learns *general* decomposition strategies from trial and error. In fact, models discover useful parallelization patterns, such as running the next step along with the self-verification of a previous step, or hedging a primary approach with a backup one, in an emergent manner that would be difficult to hand-design (Yao et al., 2023; Wu et al., 2025; Zheng et al., 2025).

Compared to BoN, APR avoids redundant computation. APR models have control over what each parallel thread will do before branching out. Therefore, APR can learn to produce a set of unique, non-overlapping subtasks before assigning them to independent threads (Wang et al., 2023; Stiennon et al., 2022; Pan et al., 2025; Yang et al., 2025).

Compared to non-adaptive approaches, APR can choose not to parallelize. Adaptive models can adjust the level of parallelization to match the complexity of the problem against the complexity and overhead of parallelization (Lian et al., 2025).

In practice, this is implemented by having the model output special tokens that control when to reason in parallel versus sequentially. Below is a condensed ThreadWeaver-style trace: two outlines and two paths under a block, then the threads agree on a single boxed answer.

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*Figure 3: Example of an Adaptive Parallel Reasoning Trajectory from ThreadWeaver, manually condensed for ease of illustration.*

*Figure 4: Special Tokens Variants across Adaptive Parallel Reasoning Papers*

Inference Systems for Adaptive Parallelism

How do we actually execute parallel branches? We take inspiration from computer systems, and specifically, multithreading and multiprocessing. Most of this work can be viewed as leveraging a fork-join design.

At inference time, we are effectively asking the model to perform a map-reduce operation:**

• Fork the problem into subtasks/threads, process them concurrently

• Join them into a final answer

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*Figure 5: Fork-join Inference Design*

Specifically, the model will encounter a list of subtasks. It will then prefill each of the subtasks and send them off as independent requests for the inference engine to process. These threads then decode concurrently until they hit an end token or exceed max length. This process blocks until all threads finish decoding and then aggregates the results. This is common across various adaptive parallel reasoning approaches. However, one issue arises during aggregation: the content generated in branches cannot be easily aggregated at the KV cache level. This is because tokens in independent threads start at identical position IDs, resulting in encoding overlap and non-standard behavior when merging KV cache back together. Similarly, since independent threads do not attend to each other, their concatenated KV cache results in a non-causal attention pattern, which the base model has not seen during training.

To address this issue, the field splits into two schools of thought on how to execute the aggregation process, defined by whether they modify the inference engine or work around it.

Multiverse modifies the inference engine to reuse KV cache across the join.** Before taking a deeper look into Multiverse (Yang et al., 2025)’s memory management, let’s first understand how KV cache is handled up until the “join” phase. Notice how each of the independent threads share the prefix sequence, i.e., the list of subtasks. Without optimization, each thread needs to prefill and recompute the KV cache for the prefix sequence. However, this redundancy can be avoided with SGLang’s RadixAttention (Sheng et al., 2023), which organizes multiple requests into a radix tree, a trie (prefix tree) with sequences of elements of varying lengths instead of single elements. This way, the only new KV cache entries are those from independent thread generation.

*Figure 6: RadixAttention’s KV Cache Management Strategy*

Now, if everything went well, all the independent threads have come back from the inference engine. Our goal is now to figure out how to synthesize them back into a single sequence to continue decoding for next steps. It turns out, we can reuse the KV cache of these independent threads during the synthesis stage. Specifically, Multiverse (Yang et al., 2025), Parallel-R1 (Zheng et al., 2025), and NPR (Wu et al., 2025) modify the inference engine to copy over the KV cache generated by each thread and edits the page table so that it stitches together non-contiguous memory blocks into a single KV cache sequence. This avoids the redundant computation of a second prefill and reuses existing KV cache as much as possible. However, this has several major limitations.

First, this approach requires modifying the inference engine to perform non-standard memory handling, which can result in unexpected behaviors. Specifically, since the synthesis request references KV cache from previous requests, it creates fragility in the system and the possibility of bad pointers. Another request can come in and evict the referenced KV cache before the synthesis request completes, requiring it to halt and trigger a re-prefilling of the previous thread request. This problem has led the Multiverse researchers (Yang et al., 2025) to limit the batch size that the inference engine can handle, which restricts throughput.

*Figure 7: KV Cache “Stitching” During Multiverse Inference*

Second, this approach modifies how models see the sequence, which creates a distributional shift that models are not pretrained on, therefore requiring more extensive training to align behavior. Specifically, when we stitch together KV cache this way, we create a sequence with non-standard position encoding. During independent-thread generation, all threads started at the same position index and attended to the prior subtasks, NOT each other. So when the threads merge back, the resulting KV cache has a non-standard positional encoding and does not use causal attention. Therefore, this approach requires extensiv