From Traces to Strategies: Training-Free Efficient Reasoning via Execution Feedback

Large reasoning models (LRMs) often exhibit overthinking, producing unnecessarily long chains of thought that increase computational cost. Existing approaches shorten reasoning through additional training or inference-time intervention, but remain limited by training cost, model access requirements, or insufficient guidance for specific problem-solving processes. To this end, we propose TraCE, a training-free framework that derives instance-specific reasoning strategies through trajectory analysis and execution feedback. TraCE extracts reusable efficiency rules from native correct reasoning trajectories and iteratively validates them through execution feedback to obtain effective rule sets. The validated rules are then consolidated into a domain-level rule repositories and further induces efficiency guidance shared within each domain. Given a new problem, TraCE first routes it to the most relevant domain, selects relevant and complementary rules, and compiles them into an instance-specific reasoning strategy. The compiled strategy is directly provided to the target model within a single problem-solving call, without updating model parameters. TraCE could be directly deployed on large-scale, production-grade flagship models. We evaluate TraCE on DeepSeek-V4-Pro and GLM-5.3-Flash across both Reasoning and Agent tasks. Experiments show that TraCE consistently reduces reasoning tokens and performs effectively across heterogeneous task settings.