Action-Informed Merging for Agentic Models

Agentic models combine reasoning with executable actions to accomplish tasks in interactive environments. Models trained for specific domains often excel within those environments, but their capabilities may not generalize to other domains. Model merging combines the parameters of separately trained models into a single model, offering a way to consolidate their domain-specific strengths into a more broadly capable agent without additional training. However, models trained for different domains may have conflicting parameter updates, making it difficult to broaden domain coverage while retaining specialist-level performance via merging. To combine complementary capabilities while limiting interference, we propose Action-Informed Merging (AIM), which combines action-relevant updates from specialist checkpoints and selects which parameters to modify and how much to change them. Specifically, AIM estimates parameter importance from squared gradients of action-token and full-response-token losses. Updates from specialist checkpoints are weighted according to parameter importance for action prediction, with less weight assigned to conflicting updates. Parameter-block selection then determines which parameters to modify and how much to change them. This selection uses full-response importance to penalize changes to sensitive parameters and constrains estimated loss increases separately for each specialist checkpoint whose capabilities should be retained. The procedure requires *neither further model training nor inference-time routing*. On tool-use benchmarks, AIM reaches a 49.11% equal-weight average of BFCL accuracy and Terminal-Bench success, compared with 47.59% for uniform averaging. Across web, mobile, desktop, and GUI grounding, its four-domain average is 50.58%, a *14.1% relative improvement* over the weighted averaging baseline.