How to Train an LLM to Retain Skills After External Procedures Are Disabled

The main goal of this work is to teach an LLM not just to use an external procedure, but to gradually integrate that skill into its own parameters. On FeatureBench, this approach yielded a gain of 8.1 percentage points, and after disabling the external scaffold, the model retained 85.2% of the performance achieved with it.

Typically, these two parts exist separately: the model is fine-tuned on data, while action sequences, tools, and other procedural hints are designed specifically for the inference stage. The authors propose linking them into an evolving graph: identifying useful skills, distilling them into the model, and dynamically recompiling procedures as training progresses.

The practical significance here is more important than the fancy term “evolution.” The external scaffold can temporarily act as a crutch or a coach: it helps the model solve more tasks, after which part of the strategy is transferred to the model itself. As a result, on the same dataset, the approach significantly outperformed standard SFT, and the model continued to solve tasks even without external support.

However, this is not yet proof of a universal method for training agents. The source paper shows results on a single benchmark, and the abstract lacks details about the graph architecture itself, the training cost, and exactly which skills were discovered. Therefore, the 8.1 percentage point improvement is an argument in favor of this direction, not a guarantee that any set of tools can be “baked” into the model’s weights with the same success.

If an external scaffold provides a significant performance boost but can later be removed, where do you draw the line between a useful training crutch and the model’s dependence on specific procedures? Source: cs.CL updates on arXiv.org