Large Language Models — The Video Signal

Jul 29, 2026 · 41:28

Hugging Face Journal Club: Kimi K3

The discussion summarizes the Kimi K3 tech report, detailing a highly complex and scaled training pipeline for large language models. Key innovations include specialized agentic structures (general, deep research, coding), multi-tier on-policy distillation across nine domain experts, and advanced infrastructure techniques like partial rollout schemes and dynamic resource scheduling. The talk emphasizes that the model's performance is achieved through sophisticated engineering efforts rather than a single breakthrough concept.

Key takeaways

Multi-Tier Expert Specialization 2:00
The training pipeline involves generating nine specialized domain experts (3 reasoning levels: low, high, max) for three general domains (general tasks, deep research, coding). These are combined using multi-tier on-policy distillation into a single student model.
Partial Rollout Scheme 4:08
A novel RL technique where the system samples $k$ rollouts from $n$ prompts. It uses an additional parameter $\lambda$ to measure completed rollouts within a budget, updating weights on that subset and pushing incomplete rollouts into subsequent iterations.
Extensible Chat Template 6:42
The Kimi K3 model utilizes an 'extensible token markup language' for its chat template. This design aims to be easy to learn during SFT while remaining flexible enough to accommodate future modalities without re-engineering the core template.
Advanced Infrastructure Scaling 30:35
The system employs sophisticated resource management, including a dynamic rollout auto throttling scheduler based on KV cache pressure and a method for collocating RL training and inference by automatically balancing GPU resources between the two tasks.

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Jul 26, 2026 · 17:31

The Messy Reality of Scale: Synthetic Data and Pre-Training — Marah Abdin & Robert McHardy, poolside

The presentation details how poolside scaled their LLM training from smaller models (Laguna M/XS) to a massive 118 billion parameter model for agentic coding. The core strategy involves creating sophisticated synthetic data pipelines—which complement organic data by exposing implicit rationale and structure—and implementing extreme rigor in the pre-training process. Key technical challenges addressed include catching silent failures like broken GPUs, numerical precision loss during tensor parallel accumulation (BF16 to FP32 fixes), and race conditions via weight hashing checks. The resulting model, Laguna S, demonstrates competitive performance against industry leaders on coding benchmarks.

Key takeaways

Synthetic Data for Feature Extraction 10:37
Synthetic data is used not to replace organic data but to complement it by extracting and projecting implicitly hidden features (e.g., implicit rationale, planning, structure). Pipelines are modular, allowing complex tasks to be broken down into stages like multi-stage workflows, cross-domain porting (e.g., math problems to code), or multi-turn chats.
Rigorous Training Validation: Trust Nothing 15:03
To ensure correctness at scale, the team implemented model replica hashing checks; if hashes over weights do not match across distributed replicas, training is immediately halted. Failures observed include silent data corruption from broken GPUs and gradient corruption due to race conditions.
Scaling Success: The 118B Parameter Model
The final model, Laguna S (118B parameters), was trained on 30 trillion tokens across 4,000 GPUs. Early results show strong performance in agentic coding benchmarks (e.g., SWEETbench), outperforming models like GLM 4.5 Air.

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Jul 22, 2026 · 10:09

How are large language models trained?

Large Language Models (LLMs) undergo two core training phases: pre-training and post-training. Pre-training uses next token prediction on massive datasets to build foundational language knowledge. Post-training refines this model for safety, helpfulness, and instruction following using techniques like Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL). The process requires complex engineering at scale, including managing petabytes of data and utilizing advanced evaluation methods like auto-raters.

Key takeaways

Pre-training: Foundational Knowledge
The initial phase trains the model using next token prediction (a self-supervised task). This allows the LLM to implicitly learn language patterns and world knowledge without manual labeling. Scaling this process is a massive engineering challenge, requiring complex parallelism across thousands of interconnected GPUs, automated recovery systems, and pipelines for petabytes of data.
Post-training: Alignment and Refinement 5:40
This phase focuses on aligning the model to be safe, helpful, and accurate. It primarily involves Supervised Fine-Tuning (SFT) using gold standard examples, or Reinforcement Learning (RL), where a separate Reward Model grades responses for quality, pushing the LLM toward desired behaviors.
Evaluation and Measurement 7:40
Measuring model success requires defining clear criteria. While pre-training measures next word prediction accuracy, post-training focuses on alignment (e.g., helpfulness). Evaluation is scaled using a second LLM—an auto rater—to score responses against defined criteria.

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Jul 16, 2026 · 36:00

New Model: Inkling by Thinking Machine on Hugging Face

Thinking Machines announced Inkling, a massive open model with nearly one trillion parameters. It is designed as a natively multimodal architecture, accepting image, text, and audio inputs simultaneously. Key technical features include a Mixture-of-Experts (MoE) structure (975B total / 41B active params), a 1M token context window, and advanced deployment support across multiple frameworks like `transformers`, SGLang, vLLM, and `llama.cpp`. The model is available in BF16 and NVFP4 formats, facilitating high-performance inference on various hardware setups.

Key takeaways

Multimodal Capability & Scale 3:50
Inkling is a true multimodal model that natively processes image, text, and audio inputs using a single architecture. It boasts an immense 1M token context window and was trained on 45T tokens.
Architectural Innovations 5:10
The model utilizes relative attention (replacing RoPE) and a specialized SConv layer to efficiently aggregate hidden states, making it highly efficient for multimodal tasks compared to previous models.
Deployment Flexibility 7:10
Inkling supports multiple deployment paths: BF16 (requiring ~2TB VRAM) and NVFP4 (600GB VRAM). It provides day-zero support for `transformers`, SGLang, vLLM, and `llama.cpp` (including GGUF quants via Unsloth), enabling diverse inference environments.

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