Efficient Skill Acquisition Without Exhaustive Datasets

Determine whether large language models, particularly agentic language models trained via post-training, can be trained to acquire new skills more efficiently than conventional inductive fine-tuning, without relying on exhaustive training datasets or processing large amounts of redundant information from already mastered examples.

Background

The paper critiques standard post-training paradigms for LLMs, which depend on large, diverse training datasets and assume i.i.d. conditions, yet often suffer from distribution shift, high computational and annotation costs, redundancy, and catastrophic forgetting. These limitations motivate exploring more efficient ways for models to acquire new skills without processing large quantities of redundant data.

The proposed test-time self-improvement framework (TT-SI) targets uncertain instances during inference, generates similar synthetic examples, and performs lightweight, ephemeral updates. The open question specifically asks if models can be trained to acquire new skills more efficiently without exhaustive datasets or redundant processing, framing the need for approaches that focus learning on informative samples.