Baseline Shape Decides the Verdict: A Controlled Re-Examination of Ternary Language Models at 60K Parameters

arXiv:2609.29397 · cs.CL · Submitted 2026-09-24 · Read on arXiv

cs.CL

Submitted: 2026-09-24

Updated: 2026-09-24

Code: https://github.com/veldanda/ByteLM

Terminology

• Transformer — 58× in this paper · explained in 2 episodes
• Quantization — 9× in this paper · explained in 12 episodes
• Embeddings — 5× in this paper · explained in 2 episodes
• Fine-Tuning — 5× in this paper · explained in 5 episodes
• Perplexity — 3× in this paper · explained in 3 episodes
• Scaling Laws — 3× in this paper · explained in 4 episodes
• Sparse Attention — 2× in this paper · explained in 5 episodes
• Tokenization — 2× in this paper · explained in 2 episodes
• Adaptation — 1× in this paper · explained in 1 episode
• Benchmark — 1× in this paper · explained in 2 episodes
• Quantization-Aware Training — 1× in this paper · explained in 3 episodes
• Verification — 1× in this paper · explained in 2 episodes

Sources

• Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation
• Hymba: A Hybrid-head Architecture for Small Language Models
• TinyStories: How Small Can Language Models Be and Still Speak Coherent English?
• Ternary Mamba: Grouped Quantization-Aware Training of W1.58A16 State Space Models
• Mamba: Linear-Time Sequence Modeling with Selective State Spaces
• Training Compute-Optimal Large Language Models
• Spectra: Surprising Effectiveness of Pretraining Ternary Language Models at Scale
• ParetoQ: Improving Scaling Laws in Extremely Low-bit LLM Quantization
• The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits
• TernaryLM: Memory-Efficient Language Modeling via Native 1.5-Bit Quantization with Adaptive Layer-wise Scaling
• Scaling Laws vs Model Architectures: How does Inductive Bias Influence Scaling?
• Mapping the Schedule x Bit-Width Boundary in Sub-100M Quantisation-Aware Training
• Spectra 1.1: Scaling Laws and Efficient Inference for Ternary Language Models

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