On Distinguishing Capability Elicitation from Capability Creation in Post-Training: A Free-Energy Perspective
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 ...
| Authors | Yuhao Li, Shengchao Liu |
|---|---|
| Primary category | cs.AI |
| Published | 8 May 2026 |
| Last updated at source | 8 May 2026 |
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