Off-Trajectory Reasoning: Can LLMs Collaborate on Reasoning Trajectory?

• URL: http://arxiv.org/abs/2510.06410v1
• Date: Tue, 07 Oct 2025 19:42:50 GMT
• Title: Off-Trajectory Reasoning: Can LLMs Collaborate on Reasoning Trajectory?
• Authors: Aochong Oliver Li, Tanya Goyal,
• Abstract summary: Reasoning LLMs are trained to verbalize their reasoning process, yielding strong gains on complex tasks.<n>A key prerequisite is the ability to assess the usefulness and build on another model's partial thinking.<n>This paper investigates the question: can standard solo-reasoning training pipelines deliver desired off-trajectory behaviors?
• Score: 13.980638430366625
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Reasoning LLMs are trained to verbalize their reasoning process, yielding strong gains on complex tasks. This transparency also opens a promising direction: multiple reasoners can directly collaborate on each other's thinking within a shared trajectory, yielding better inference efficiency and exploration. A key prerequisite, however, is the ability to assess the usefulness and build on another model's partial thinking -- we call this off-trajectory reasoning. Our paper investigates a critical question: can standard solo-reasoning training pipelines deliver desired off-trajectory behaviors? We propose twin tests that capture the two extremes of the off-trajectory spectrum, namely Recoverability, which tests whether LLMs can backtrack from "distractions" induced by misleading reasoning traces, and Guidability, which tests their ability to build upon correct reasoning from stronger collaborators. Our study evaluates 15 open-weight LLMs (1.5B-32B) and reveals a counterintuitive finding -- "stronger" LLMs on benchmarks are often more fragile under distraction. Moreover, all models tested fail to effectively leverage guiding steps from collaborators on problems beyond their inherent capabilities with solve rates remaining under 9.2%. Finally, we conduct control studies to isolate the effects of three factors in post-training on these behaviors: the choice of distillation teacher, the use of RL, and data selection strategy. Our results provide actionable insights for training natively strong reasoning collaborators; e.g., we find that suboptimal recoverability behaviors of teacher models are transferred to distilled students even if the distillation trajectories are correct. Taken together, this work lays the groundwork for evaluating multi-model collaborations in shared reasoning trajectories and highlights the limitations of off-the-shelf reasoning LLMs.

Related papers

• Towards Generalizable Reasoning: Group Causal Counterfactual Policy Optimization for LLM Reasoning [50.352417879912515]
  Large language models (LLMs) excel at complex tasks with advances in reasoning capabilities.<n>We propose Group Causal Counterfactual Policy Optimization to explicitly train LLMs to learn generalizable reasoning patterns.<n>We then construct token-level advantages from this reward and optimize the policy, encouraging LLMs to favor reasoning patterns that are process-valid and counterfactually robust.
  arXiv  Detail & Related papers  (2026-02-06T08:03:11Z)
• When Actions Teach You to Think: Reasoning-Action Synergy via Reinforcement Learning in Conversational Agents [2.689316553293938]
  Supervised fine-tuning (SFT) has emerged as one of the most effective ways to improve the performance of large language models (LLMs) in downstream tasks.<n>We propose a pipeline in which LLMs generate reasoning steps that guide both the invocation of tools and the final answer generation for conversational agents.
  arXiv  Detail & Related papers  (2025-12-12T04:44:40Z)
• From <Answer> to <Think>: Multidimensional Supervision of Reasoning Process for LLM Optimization [62.07990937720985]
  Dimension-level Reward Model (DRM) is a new supervision framework for Large Language Models.<n>DRM evaluates the quality of a reasoning process along three fundamental, complementary, and interpretable dimensions.<n> Experimental results show that DRM provides effective supervision signals, guides the optimization of LLMs and enhances their reasoning ability.
  arXiv  Detail & Related papers  (2025-10-13T14:29:15Z)
• FairReason: Balancing Reasoning and Social Bias in MLLMs [54.26091556079722]
  Multimodal Large Language Models (MLLMs) already achieve state-of-the-art results across a wide range of tasks and modalities.<n>Recent studies explore advanced prompting schemes and post-training fine-tuning to push their reasoning ability further.
  arXiv  Detail & Related papers  (2025-07-30T19:57:22Z)
• Consistent Paths Lead to Truth: Self-Rewarding Reinforcement Learning for LLM Reasoning [87.7836502955847]
  We propose a novel self-rewarding reinforcement learning framework to enhance Large Language Model (LLM) reasoning.<n>Our key insight is that correct responses often exhibit consistent trajectory patterns in terms of model likelihood.<n>We introduce CoVo, an intrinsic reward mechanism that integrates Consistency and Volatility via a robust vector-space aggregation strategy.
  arXiv  Detail & Related papers  (2025-06-10T12:40:39Z)
• Think or Not? Selective Reasoning via Reinforcement Learning for Vision-Language Models [67.87579664988199]
  TON is a two-stage training strategy for vision-language models (VLMs)<n>It introduces a think-or-not format that serves as a cold start for selective reasoning.<n>TON can reduce the completion length by up to 90% compared to vanilla GRPO.
  arXiv  Detail & Related papers  (2025-05-22T16:13:29Z)
• ToTRL: Unlock LLM Tree-of-Thoughts Reasoning Potential through Puzzles Solving [4.987786842464663]
  Tree-of-thoughts (ToT) offers a conceptually more advanced approach by modeling reasoning as an exploration within a tree structure.<n>ToTRL is designed to guide LLMs in developing the parallel ToT strategy based on the sequential CoT strategy.<n>Our ToTQwen3-8B model, trained with ToTRL, achieves significant improvement in performance and reasoning efficiency on complex reasoning tasks.
  arXiv  Detail & Related papers  (2025-05-19T05:18:58Z)
• Satori: Reinforcement Learning with Chain-of-Action-Thought Enhances LLM Reasoning via Autoregressive Search [57.28671084993782]
  Large language models (LLMs) have demonstrated remarkable reasoning capabilities across diverse domains.<n>Recent studies have shown that increasing test-time computation enhances LLMs' reasoning capabilities.<n>We propose a two-stage training paradigm: 1) a small-scale format tuning stage to internalize the COAT reasoning format and 2) a large-scale self-improvement stage leveraging reinforcement learning.
  arXiv  Detail & Related papers  (2025-02-04T17:26:58Z)
• Learning from Committee: Reasoning Distillation from a Mixture of Teachers with Peer-Review [11.756344944226495]
  We introduce a novel Fault-Aware DistIllation via Peer-Review (FAIR) approach.<n>Instead of merely obtaining rationales from teachers, our method asks teachers to identify and explain the student's mistakes.<n>Our method reduces the chance of teachers guessing correctly with flawed rationale.
  arXiv  Detail & Related papers  (2024-10-04T17:59:41Z)

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.