BoLT is a benchmark of surrogate models fitted to real LLM experiment data that enables evaluation of Bayesian and black-box optimization methods on multi-fidelity, multi-objective, high-dimensional LLM tasks.
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Predictable scale: Part i–optimal hyperparameter scaling law in large language model pretraining
11 Pith papers cite this work, alongside 1 external citations. Polarity classification is still indexing.
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MultiHashFormer enables hash-based autoregression in LMs by encoding tokens as multi-hash signatures, outperforming standard Transformers at 100M-3B scales while keeping parameter count constant for multilingual expansion.
Optimal learning rate for models from 22M to 707M parameters shows nonlinear upward curvature with scale that disappears under effective learning rate and data-scale extrapolation.
Optimal hyperparameters for LLM continued pre-training follow predictable scaling laws derived from proxy models, enabling a two-stage framework that predicts settings from compute budget and checkpoint state to reduce search overhead by 90%.
A framework quantifies hyperparameter transfer via scaling-law fit quality, extrapolation robustness, and loss penalty, with ablations showing that μP's advantage over standard parameterization stems from maximizing the embedding layer learning rate to avoid bottlenecks and instabilities in AdamW.
AutoLLMResearch trains agents in a multi-fidelity LLMConfig-Gym environment formulated as a long-horizon MDP to enable cross-fidelity extrapolation for automating high-cost LLM experiment configurations.
HyperP transfers optimal learning rates across model width, depth, tokens, and MoE granularity under Frobenius-sphere constraints, delivering stable scaling and 1.58x efficiency gains.
MoE models with activation rates in an optimal region outperform dense LLMs of identical total parameter count, training compute, and data budget, with the optimal region consistent across scales.
Proposes a three-term scaling law for model size, training steps and batch size that recovers optimal batch size scaling and can be fitted using fewer runs by incorporating suboptimal batch sizes.
Technical report announcing Ling-2.6 and Ring-2.6 models with hybrid linear attention, evolutionary CoT, and KPop RL for efficient agentic intelligence at scale.
Staged factorial screening recovers stable early penalties from total batch, depth, and width in 2-10 minute pretraining runs and supports a bridge-centered recommendation through 24-hour continuations on two hosts.
citing papers explorer
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BoLT: A Benchmark to Democratize Black-box Optimization Research for Expensive LLM Tasks
BoLT is a benchmark of surrogate models fitted to real LLM experiment data that enables evaluation of Bayesian and black-box optimization methods on multi-fidelity, multi-objective, high-dimensional LLM tasks.
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MultiHashFormer: Hash-based Generative Language Models
MultiHashFormer enables hash-based autoregression in LMs by encoding tokens as multi-hash signatures, outperforming standard Transformers at 100M-3B scales while keeping parameter count constant for multilingual expansion.
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On the Nonlinearity of Learning Rate Scaling for LLM Training
Optimal learning rate for models from 22M to 707M parameters shows nonlinear upward curvature with scale that disappears under effective learning rate and data-scale extrapolation.
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Predictable Scaling Laws of Optimal Hyperparameters for LLM Continued Pre-training
Optimal hyperparameters for LLM continued pre-training follow predictable scaling laws derived from proxy models, enabling a two-stage framework that predicts settings from compute budget and checkpoint state to reduce search overhead by 90%.
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Quantifying Hyperparameter Transfer and the Importance of Embedding Layer Learning Rate
A framework quantifies hyperparameter transfer via scaling-law fit quality, extrapolation robustness, and loss penalty, with ablations showing that μP's advantage over standard parameterization stems from maximizing the embedding layer learning rate to avoid bottlenecks and instabilities in AdamW.
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AutoLLMResearch: Training Research Agents for Automating LLM Experiment Configuration - Learning from Cheap, Optimizing Expensive
AutoLLMResearch trains agents in a multi-fidelity LLMConfig-Gym environment formulated as a long-horizon MDP to enable cross-fidelity extrapolation for automating high-cost LLM experiment configurations.
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Rethinking Language Model Scaling under Transferable Hypersphere Optimization
HyperP transfers optimal learning rates across model width, depth, tokens, and MoE granularity under Frobenius-sphere constraints, delivering stable scaling and 1.58x efficiency gains.
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Mixture-of-Experts Can Surpass Dense LLMs Under Strictly Equal Resource
MoE models with activation rates in an optimal region outperform dense LLMs of identical total parameter count, training compute, and data budget, with the optimal region consistent across scales.
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How to Allocate Your Tokens? Scaling Laws with Training Steps and Batch Size
Proposes a three-term scaling law for model size, training steps and batch size that recovers optimal batch size scaling and can be fitted using fewer runs by incorporating suboptimal batch sizes.
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Ling and Ring 2.6 Technical Report: Efficient and Instant Agentic Intelligence at Trillion-Parameter Scale
Technical report announcing Ling-2.6 and Ring-2.6 models with hybrid linear attention, evolutionary CoT, and KPop RL for efficient agentic intelligence at scale.
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Staged Factorial Screening for Budget-Constrained Micro-Pretraining
Staged factorial screening recovers stable early penalties from total batch, depth, and width in 2-10 minute pretraining runs and supports a bridge-centered recommendation through 24-hour continuations on two hosts.