Proxy models reveal transferable SFT-RL budget allocation rules across scales
Source published ·Modelwire updated
Original coverage: arXiv cs.LG ↗·How Modelwire adds context
The development
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’s AI-generated summary of coverage from arXiv cs.LG.
Modelwire analysis
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The 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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Coverage behind this analysis
These archive entries ground the connection in our analysis. They are ordered by source publication date, with links to our coverage and the original sources.
·arXiv cs.CL
LLM judges fail to predict human disagreement patterns
Researchers expose a critical gap in how LLM evaluators are benchmarked. While large language models match human performance on binary classification tasks, they fail to capture the nuance of human disagreement itself. The work pivots evaluation methodology from comparing against collapsed ground truth to predicting full distributions of human judgment. This matters because real-world annotation…
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.