How do labs train a frontier, multi-billion parameter model? We look towards seven open-weight frontier models: Hugging Face’s SmolLM3, Prime Intellect’s Intellect 3, Nous Research’s Hermes 4, OpenAI’s gpt-oss-120b, Moonshot’s Kimi K2, DeepSeek’s DeepSeek-R1, and Arcee’s Trinity series. This blog is an attempt at distilling the techniques, motivations, and considerations used to train their models with an emphasis on training methodology over infrastructure.

LLMs trained on web-scale corpora inadvertently memorize and leak personally identifiable information (PII) present in their training data. We investigate inference-time interventions to suppress this privacy leakage. We evaluate three editing strategies: activation patching with computed steering vectors (APNEAP), random Gaussian noise steering, and Spectral Editing of Activations (SEA). Using the Enron email corpus with GPT-Neo-1.3B and finetuned Qwen3-8B-enron, we measure targeted PII suppression via exposure metrics like mean reciprocal rank (MRR), and utility via perplexity.

Can RL agents learn to play spatial reasoning puzzle games as well as, or better than, LLMs? We develop a complete RL pipeline by developing an environment for fruit box (a grid-based reasoning game) using Prime Intellect’s verifiers library, benchmarking LLMs like gpt-5.1 and gemini-3-pro, and training RL agents with SFT and GRPO to play. Repo here.

RLHF with PPO from scratch and lots of fine-tuning GPT-2 models for movie sentiment classification. Transformer environments, adapative KL control, logit/temperature scaling, whitening, and more. Full implementation here.