TraceBridge: Interaction-Aware Trajectory Rewriting across Harnesses for Coding Agent Training

Coding agents benefit from supervised fine-tuning (SFT) on high-quality interaction trajectories. However, publicly available trajectories span heterogeneous agent harnesses, complicating their reuse for training: source calls may be incompatible with the target interface, while removing unsupported interactions can discard findings needed for subsequent actions. We introduce TraceBridge, an interaction-aware trajectory rewriting system that considers source actions together with their preceding context and associated feedback to determine what becomes target-action supervision and what remains as context. Using tool contracts and recorded evidence, it compiles supported operations into target calls and retains unmapped interactions as historical context with zero direct loss. Using the same 10,000 heterogeneous trajectories for Qwen3-8B SFT, TraceBridge outperforms direct source training and Agent Data Protocol (ADP), the strongest baseline in our comparison, across OpenHands and SWE-agent on SWE-bench Verified, Pro, and Multilingual. Over three evaluation runs, mean Verified resolved rates reach 25.07% and 26.73%, improving over direct source training by 12.40 and 7.40 percentage points, respectively. Relative to ADP, OpenHands behavior in one evaluation run shows lower call rejection (3.15% to 1.47%) and fewer empty patches (39.08% to 18.07%). These results demonstrate the training value of interaction-aware trajectory rewriting. We have publicly released our code at https://anonymous.4open.science/r/trace-bridge-308E.