[2510.06410] Off-Trajectory Reasoning: Can LLMs Collaborate on Reasoning Trajectory?

Computer Science > Artificial Intelligence arXiv:2510.06410 (cs) [Submitted on 7 Oct 2025 (v1), last revised 2 Mar 2026 (this version, v2)] Title:Off-Trajectory Reasoning: Can LLMs Collaborate on Reasoning Trajectory? Authors:Aochong Oliver Li, Tanya Goyal View a PDF of the paper titled Off-Trajectory Reasoning: Can LLMs Collaborate on Reasoning Trajectory?, by Aochong Oliver Li and 1 other authors View PDF HTML (experimental) 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 und...