arXiv:2605.31175v1 Announce Type: new Abstract: The annealing phase is a pivotal convergence stage in LLM pre-training that ultimately determines final model quality. However, effectively selecting training data during this phase remains a key challenge. Current strategies rely on empirical heuristics, such as domain filtering or context extension, which lack a principled grounding in optimization theory. In this work, we characterize the annealing phase through the lens of the loss landscape's spectral geometry. We argue that optimal convergence requires gradient updates to satisfy heterogene
The paper addresses a critical challenge in LLM development as the industry pushes for more efficient and performant models, making principled approaches to training data selection a timely focus.
Improving the annealing phase of LLM pre-training directly impacts the efficiency, quality, and cost of developing advanced AI models, which is crucial for competitive advantage in the AI race.
This work proposes a theoretically grounded approach to data selection for LLM annealing, moving beyond empirical heuristics and potentially leading to more robust and higher-performing models with reduced computational overhead.
- · AI model developers
- · Cloud computing providers (reduced training costs)
- · AI-dependent industries
- · Developers relying solely on brute-force scaling
- · Companies with inefficient training pipelines
More efficient LLM pre-training leads to faster development cycles and lower computational costs for new AI models.
Access to high-quality, efficient LLMs becomes more democratized, fostering innovation across a wider range of applications.
The competitive landscape for AI model development intensifies as efficiency becomes a key differentiator, potentially leading to faster advancements in AI capabilities globally.
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