Proxy models reveal transferable SFT-RL budget allocation rules across scales
Researchers have identified a practical framework for allocating annotation budgets between supervised fine-tuning and reinforcement learning during LLM post-training, moving beyond vague heuristics. Rather than seeking a single optimal ratio, the work maps a near-optimal region of allocations that remain within 2-10% of peak performance. Critically, this region widens as models scale and transfers reliably from small proxy models to large ones, enabling practitioners to use cheaper small-model experiments to guide budget decisions for production-scale systems. This addresses a concrete operational bottleneck in LLM development where annotation resources are finite and allocation choices directly impact training efficiency.
Modelwire context
ExplainerThe key insight isn't just that a near-optimal region exists, but that it *expands* as models scale. This means practitioners can stop chasing a single magic ratio and instead use small-model experiments as a cheap proxy to bound decisions for production systems, collapsing what was previously an expensive trial-and-error process.
This connects directly to the post-hoc LLM-judge alignment work from early September, which exposed how teams have been optimizing for the wrong target in evaluation. Here, the same pattern emerges in training: practitioners have been treating SFT-RL allocation as a binary search problem when it's actually a bounded region problem. The document VLM cost-quality paper also shares the operational DNA: both show that careful resource allocation on production data beats brute-force scaling. The finding also echoes the GLM 5.3 Flash result that bigger doesn't require proportional investment; here, the message is that annotation budgets don't need perfect precision, just informed bounds.
If teams at scale (70B+) report that allocations derived from 7B proxy experiments land within the predicted near-optimal region on their actual production tasks within the next six months, the transferability claim holds and this becomes a standard pre-training playbook. If instead production allocations drift significantly from proxy predictions, the work remains a useful heuristic but not a reliable transfer mechanism.
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MentionsLLM · SFT · RL
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Modelwire summarizes, we don’t republish. arXiv cs.LG originally reported this story as “Scaling Near-Optimal SFT-RL Annotation Budget Allocation from Small to Large LLMs”. The full content lives on arxiv.org. If you’re a publisher and want a different summarization policy for your work, see our takedown page.