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

arXiv cs.AI Papers
post-training llm-theory capability-elicitation reinforcement-learning free-energy-principle

Summary

This paper proposes a framework to distinguish between capability elicitation and creation in large language model post-training using a free-energy perspective, arguing that supervised fine-tuning and reinforcement learning often reweight existing behaviors rather than creating new ones.

arXiv:2605.08368v1 Announce Type: new 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.
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# On Distinguishing Capability Elicitation from Capability Creation in Post-Training: A Free-Energy Perspective
Source: [https://arxiv.org/abs/2605.08368](https://arxiv.org/abs/2605.08368)
[View PDF](https://arxiv.org/pdf/2605.08368)

> 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\.

## Submission history

From: Yuhao Li \[[view email](https://arxiv.org/show-email/00adf26b/2605.08368)\] **\[v1\]**Fri, 8 May 2026 18:23:25 UTC \(55 KB\)

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