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Show HN: Latent-free ternary LLM training
无需隐变量，三值量化LLM训练新方法，开源项目BitBop让你以极低成本体验高效训练。
Article URL: https://github.com/ValerioDolci/bitbop Comments URL: https://news.ycombinator.com/item?id=48892013 Points: 1 # Comments: 1
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Geometrically Principled Randomized Optimization for Efficient LLM Training
基于几何原理的随机优化方法，为大规模语言模型训练效率提供新思路
arXiv:2510.01878v2 Announce Type: replace Abstract: Low-rank gradient optimization for large language models is currently divided into two categories:…
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POET-X: Memory-efficient LLM Training by Scaling Orthogonal Transformation
ICML 2026 Oral论文，提出通过扩展正交变换实现大模型训练的内存高效方案。
arXiv:2603.05500v2 Announce Type: replace Abstract: Efficient and stable training of large language models (LLMs) remains a core challenge in modern m…
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Reformulate LLM Reinforcement Learning for Efficient Training under Black-box Discrepancy
当LLM强化学习遭遇黑盒差异，这篇论文提出重构框架实现更高效训练。
arXiv:2606.08779v1 Announce Type: new Abstract: Reinforcement Learning (RL) has emerged as a pivotal post-training paradigm, yet it frequently suffers…
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Sample-Efficient Post-Training for LEGO Spatial-Physics Reasoning
提出样本高效后训练方法，突破乐高空间物理推理任务的数据瓶颈
arXiv:2606.07602v1 Announce Type: new Abstract: LLM-based LEGO assembly generation requires both semantic grounding and physical feasibility. We ident…
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Speedrunning Tabular Foundation Model Pretraining
一篇关于加速表格数据基础模型预训练的前沿研究，提出创新策略大幅提升训练效率。
arXiv:2606.03681v1 Announce Type: new Abstract: Pretraining cost is a major bottleneck for research on tabular foundation models, slowing the iteratio…
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Efficient Pre-Training with Token Superposition
提出Token叠加技术，颠覆预训练效率瓶颈，大幅降低算力需求，LLM训练优化必读。
arXiv:2605.06546v2 Announce Type: replace Abstract: Pre-training of Large Language Models is often prohibitively expensive and inefficient at scale, r…