Planner and Executor: Collaboration between Discrete Diffusion And Autoregressive Models in Reasoning

• URL: http://arxiv.org/abs/2510.15244v2
• Date: Mon, 20 Oct 2025 05:20:30 GMT
• Title: Planner and Executor: Collaboration between Discrete Diffusion And Autoregressive Models in Reasoning
• Abstract summary: Current autoregressive language models (ARMs) achieve high accuracy but require long token sequences, making them costly.<n>DDLMs enable parallel and flexible generation within a fixed number of steps.<n>We present a study exploring hybrid architectures that couple DDLMs with ARMs to assess whether their collaboration can yield complementary benefits.
• Score: 40.69037032768994
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Current autoregressive language models (ARMs) achieve high accuracy but require long token sequences, making them costly. Discrete diffusion language models (DDLMs) enable parallel and flexible generation within a fixed number of steps and have recently emerged for their strong performance in complex reasoning and long-term planning tasks. We present a study exploring hybrid architectures that couple DDLMs with ARMs to assess whether their collaboration can yield complementary benefits. We first examine collaboration in text space, where one model plans the reasoning process and another executes the final answer based on that plan. We then extend this setup to latent-space communication, introducing a learned projector that maps DDLM latents into the ARM's embedding space, potentially bypassing some of the text-generation limitations of diffusion models. We find that shifting DDLM --> ARM communication from text space to latent space yields significant accuracy gains, for example increasing from 27.0% to 54.0% on DART-5 and from 0.0% to 14.0% on AIME24. We also find that combining a DDLM planner with an ARM executor can provide substantial computational savings with little to no impact on accuracy. For example, the latent-space pipeline, using 64 tokens for planning and roughly 5 for execution, surpasses Qwen3.1-7B on DART-5 and AIME, despite Qwen using 44 times more tokens. Overall, our study offers new insights into reasoning with DDLMs and highlights their potential in hybrid architectures.

Related papers

• Diffusion Language Model Parallel Decoding via Product-of-Experts Bridge [93.37920675145553]
  Diffusion language models (DLMs) offer substantial speed advantages through parallel decoding.<n>The lack of token dependencies limits generation quality compared to autoregressive (AR) models.<n>Recent progress attempts to bridge the gap via importance sampling, with DLM being the proposal and AR being the target.<n>We introduce PoE-Bridge, a novel decoding framework that drastically improves generation speed and accuracy.
  arXiv  Detail & Related papers  (2026-06-06T08:21:06Z)
• Introspective Diffusion Language Models [58.91876345013321]
  Introspective Diffusion Language Model (I-DLM) is a paradigm that retains parallel decoding while inheriting introspective consistency of AR training.<n>I-DLM uses a novel introspective strided decoding (ISD) algorithm, which enables the model to verify previously generated tokens while advancing new ones in the same forward pass.<n>I-DLM is the first DLM to match the quality of its same-scale AR counterpart while outperforming prior DLMs in both model quality and practical serving efficiency across 15 benchmarks.
  arXiv  Detail & Related papers  (2026-04-13T06:01:01Z)
• Latent-DARM: Bridging Discrete Diffusion And Autoregressive Models For Reasoning [40.43847739412248]
  We introduce Latent-DARM, a latent-space communication framework bridging DDLM (planners) and ARM (executors)<n>Across mathematical, scientific, and commonsense reasoning benchmarks, Latent-DARM outperforms text-based interfaces on average.<n>Latent-DARM approaches the results of state-of-the-art reasoning models while using less than 2.2% of its token budget.
  arXiv  Detail & Related papers  (2026-03-10T04:45:26Z)
• Residual Context Diffusion Language Models [90.07635240595926]
  Residual Context Diffusion (RCD) is a module that converts discarded token representations into contextual residuals and injects them back for the next denoising step.<n>RCD consistently improves frontier dLLMs by 5-10 points in accuracy with minimal extra computation overhead.
  arXiv  Detail & Related papers  (2026-01-30T13:16:32Z)
• TiDAR: Think in Diffusion, Talk in Autoregression [59.94106070312094]
  TiDAR is a sequence-level hybrid architecture that drafts tokens (Thinking) in Diffusion and samples final outputs (Talking) AutoRegressively.<n> TiDAR is the first architecture to close the quality gap with AR models while delivering 4.71x to 5.91x more tokens per second.
  arXiv  Detail & Related papers  (2025-11-12T02:59:33Z)
• Beyond Next-Token Prediction: A Performance Characterization of Diffusion versus Autoregressive Language Models [82.87985794856803]
  Large Language Models (LLMs) have achieved state-of-the-art performance on a broad range of Natural Language Processing (NLP) tasks.<n>Recently, Diffusion Language Models (DLMs) have emerged as a promising alternative architecture.
  arXiv  Detail & Related papers  (2025-10-05T10:50:52Z)
• Explore-Execute Chain: Towards an Efficient Structured Reasoning Paradigm [8.405729585427226]
  Chain-of-Thought (CoT) and its variants have markedly advanced the reasoning abilities of Large Language Models (LLMs)<n>We propose the Explore-Execute Chain ($E2C$), a structured reasoning framework that decouples reasoning into two distinct phases.
  arXiv  Detail & Related papers  (2025-09-28T15:48:40Z)
• DeltaLLM: A Training-Free Framework Exploiting Temporal Sparsity for Efficient Edge LLM Inference [19.987309147268586]
  We present DeltaLLM, a training-free framework that exploits temporal sparsity in attention patterns to enable efficient LLM inference on resource-constrained edge devices.<n>We evaluate our framework on the edge-device-friendly BitNet-b1.58-2B-4T model and Llama3.2-1B-Instruct model across diverse language tasks.
  arXiv  Detail & Related papers  (2025-07-25T18:23:18Z)
• TL;DR: Too Long, Do Re-weighting for Efficient LLM Reasoning Compression [55.37723860832064]
  We propose a dynamic ratio-based training pipeline that does not rely on sophisticated data annotations.<n>We validate our approach across models on DeepSeek-R1-Distill-7B and DeepSeek-R1-Distill-14B and on a diverse set of benchmarks with varying difficulty levels.
  arXiv  Detail & Related papers  (2025-06-03T09:23:41Z)
• Reviving Any-Subset Autoregressive Models with Principled Parallel Sampling and Speculative Decoding [55.2480439325792]
  In arbitrary-order language models, it is an open question how to sample tokens in parallel from the correct joint distribution.<n>We find that a different class of models, any-subset autoregressive models (AS-ARMs), holds the solution.<n>We show that AS-ARMs achieve state-of-the-art performance among sub-200M parameter models on infilling benchmark tasks, and nearly match the performance of models 50X larger on code generation.
  arXiv  Detail & Related papers  (2025-04-29T06:33:13Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.