SFT vs RL Is the Wrong Question in Post-Training
A new arXiv paper reframes the debate around 'accessible support', not training method.
A paper posted to arXiv reframes how the field should think about post-training. The central claim: supervised fine-tuning and reinforcement learning both reweight a pretrained model's reference distribution. The real question is whether that reweighting stays inside behaviors the model could already reach, or expands that reachable set.
The authors introduce the concept of 'accessible support', the set of behaviors a model can practically produce under finite compute budgets. Training that shifts probability mass within that support is capability elicitation. Training that changes the support itself is capability creation. The free-energy framing treats demonstration signals and reward signals as two versions of the same mechanism, not fundamentally different operations.
This matters for anyone building on top of foundation models. Labs, operators, and evaluators have used SFT versus RL as a proxy for 'imitation versus discovery.' That proxy is now contested. The paper argues search, interaction, tool use, and new information are the real levers for capability creation. Watch how safety evaluators and post-training teams update their benchmarks in response.
Analysis
Elicitation versus creation is the contrast that now governs post-training investment decisions. Labs that conflate the two will misread what their fine-tuning actually buys.
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I just read this AI news story and want to understand it in my own context. Title: SFT vs RL Is the Wrong Question in Post-Training Summary: Researchers argue post-training debates misplace the key distinction. What matters is whether training expands a model's reachable behaviors, not whether the method is SFT or RL. Category: Research Source: arXiv.org, https://arxiv.org/abs/2605.08368 Using my own history and context, help me understand: 1. What is the core development and why does it matter? 2. Who are the major players involved and what are their motivations? 3. How does this fit into the broader AI landscape right now? 4. How does this apply to my own work, and what should I do or watch next? Be specific and plain spoken.
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