On Distinguishing Capability Elicitation from Capability Creation in Post-Training: A Free-Energy Perspective

2026-05-08

AI Summary

This work addresses the common oversimplification in distinguishing post-training methods for large language models—such as supervised fine-tuning (SFT) and reinforcement learning (RL)—by clarifying the distinction between “capability elicitation” and “capability creation.” Drawing on the free energy principle, the paper introduces the notion of an “accessible support set” and formalizes post-training as a reweighting of the pretraining distribution: reweighting within the support set corresponds to eliciting existing capabilities, whereas expanding the support set enables genuine capability creation. This framework transcends the conventional dichotomy based on SFT versus RL, revealing that both approaches primarily elicit rather than create capabilities under limited updates. It thus provides a novel theoretical foundation for understanding and designing post-training algorithms grounded in changes to behavioral reachability.

Abstract

Debates about large language model post-training often treat supervised fine-tuning (SFT) as imitation and reinforcement learning (RL) as discovery. But this distinction is too coarse. What matters is whether a training procedure increases the probability of behaviors the pretrained model could already produce, or whether it changes what the model can practically reach. We argue that post-training research should distinguish between capability elicitation and capability creation. We make this distinction operational by introducing the notion of accessible support: the set of behaviors that a model can practically produce under finite budgets. Post-training that reweights behaviors within this support is capability elicitation; whereas changing the support itself corresponds to capability creation. We develop this argument through a free-energy view of post-training. SFT and RL can both be seen as reweighting a pretrained reference distribution, only with different external signals. Demonstration signals define low-energy behavior for SFT, and reward signals define low-energy behavior for RL. When the update remains close to the base model, the main effect is local reweighting, not capability creation. Within this framework, the central question is no longer whether post-training is framed as SFT or RL, but whether it reweights behaviors already within reach, or instead expands the model's reachable behavioral space through search, interaction, tool use, or the incorporation of new information.

Problem

• capability elicitation
• capability creation
• post-training
• accessible support
• large language models

Innovation

• capability elicitation
• capability creation
• accessible support
• free-energy principle
• post-training