AR
arXiv CS.AI
5/12/2026

On Distinguishing Capability Elicitation from Capability Creation in Post-Training: A Free-Energy Perspective
Short summary
Researchers propose distinguishing capability elicitation (reweighting existing model behaviors) from capability creation (expanding what a model can practically reach) using a free-energy framework. Both SFT and RL primarily reweight pretrained distributions; true capability creation requires search, interaction, tool use, or new information incorporation. This framework shifts focus from SFT vs. RL framing to whether post-training expands the model's behavioral support.
- •Introduces 'accessible support' concept to formalize what behaviors a model can practically produce
- •Shows SFT and RL both operate via distribution reweighting, not fundamentally different mechanisms
- •Argues true capability creation requires expanding reachable behaviors via search, tools, or interaction
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