Scaling Laws

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On the Scaling of PEFT: Towards Million Personal Models of Trillion Parameters
Jun 1, 2026Mind Lab, :, Vin Bo +64Parameter-Efficient Fine-Tuning Billion-Parameter Models
DFlare: Scaling Up Draft Capacity for Block Diffusion Speculative Decoding
Jun 1, 2026Jiebin Zhang, Zhenghan Yu, Song Liu +9Speculative Decoding LLM Inference Efficiency
Provable Data Scaling Law for Meta Learning via Complexity Minimization
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ProbeScale: Probing Analysis to Optimize Neural Scaling Laws for Efficient Small Language Model Inference
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OctoT2I: A Self-Evolving Agentic Text-to-Image Router
Jun 1, 2026Xu Jiang, Bin Chen, Gehui Li +3Text-To-Image Real-Time Inference
ATLAS: Agentic Test-time Learning-to-Allocate Scaling
Jun 1, 2026Peijia Qin, Qi Cao, Pengtao XieTest-Time Scaling Large Language Model Reasoning
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May 30, 2026Qiao Xiao, Boqian Wu, Patrik Okanovic +6Sparsity Adam
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Balancing Learning Rates Across Layers: Exact Two-Step Dynamics and Optimal Scaling in Linear Neural Networks
May 29, 2026Tianyu Pang, Vignesh Kothapalli, Shenyang Deng +3Learning Rates Two-Layer Neural Networks
Large-scale Uncertainty Quantification for Latent Variable Models Using Subsampling Markov Chain Monte Carlo
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Spectral Reach: Understanding Neural Scaling as Progress into the Spectral Tail
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May 29, 2026Ruiqi Yu, Yiwen Wang, Yuan Hao +2Adaptive Locomotion Adversarial Motion Priors
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May 27, 2026Xinyu Wang, Mingze Li, Sicheng Lyu +6Vision-Language-Action Models Extreme Low-Bit Quantization
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May 26, 2026Xinglin Wang, Hao Lin, Shaoxiong Feng +9Test-Time Scaling Parallel
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May 26, 2026Zeyi Huang, Xuehai He, LiLiang Ren +8Transformer Architectures Recurrent Model
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May 26, 2026Xiao-Wen Yang, Ziyu Han, Xi-Hua Zhang +4Efficient Latent Reasoning Large Models
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ExComm: Exploration-Stage Communication for Error-Resilient Agentic Test-Time Scaling
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May 21, 2026Gang Dai, Yining Huang, Yiming Xia +2Test-Time Scaling Reward Gradients
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Same Architecture, Different Capacity: Optimizer-Induced Spectral Scaling Laws
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Dropout Universality: Scaling Laws and Optimal Scheduling at the Edge-of-Chaos
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Equilibrium Reasoners: Learning Attractors Enables Scalable Reasoning
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Terminal-World: Scaling Terminal-Agent Environments via Agent Skills
May 20, 2026Zihao Cheng, Hongru Wang, Zeming Liu +6Task Synthesis Single-Agent Baselines
SmoCap: Unified Scale-Pose Canonicalization with Proxy-Mapped Trust-Region QP
May 20, 2026Shihao Li, Naohiko SugitaMotion Capture Neural Scaling Laws
HRM-Text: Efficient Pretraining Beyond Scaling
May 20, 2026Guan Wang, Changling Liu, Chenyu Wang +6Large Language Model Pretraining Model Pretraining

Jun 1, 2026 · cs.LGJ/K move · Enter open · S save

On the Scaling of PEFT: Towards Million Personal Models of Trillion Parameters

Mind Lab, :, Vin Bo, Song Cao+63

Parameter-efficient fine-tuning (PEFT) is usually treated as a cheaper alternative to full fine-tuning. We study a broader role: small trainable adapters as persistent local state on top of strong shared foundation models. In this framing, the base model provides shared competence while adapters carry instance-specific behavior such as preferences, skills, tool habits, and memory-like updates. We organize the problem around three scaling axes: Scale Up, where stronger shared priors make small local updates more useful; Scale Down, where we study how small adapters can be while remaining reliable; and Scale Out, where many persistent adapted instances coexist. MinT provides one infrastructure example for managing adapter identity, revision, provenance, evaluation, and serving residency. Together, the results suggest that PEFT can be a compact substrate for persistent personal models rather than only a budget substitute for full fine-tuning.

Parameter-Efficient Fine-Tuning Billion-Parameter Models Persistent State Foundation Model Neural Scaling Laws

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