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A developer trained a 1.1B-parameter LLM from scratch on 20B tokens for about $200, using fineweb-edu for pretraining and LoRA finetuning on OpenHermes. The project includes open-source code, model weights, and a demo website.
WebGrader is a research paper introducing a self-evolving programmatic grader that derives interaction flows as executable contracts, enabling reinforcement learning for LLM web development. It improves functional success rates on benchmarks and outperforms several strong baselines.
Presents counterfactual recoverability as an outcome-grounded decision variable for on-policy distillation, showing that selectively supervising based on recoverability outperforms divergence-only methods across AIME and GPQA benchmarks.
This paper introduces AdaMTP, an adaptive training paradigm for multi-token prediction that dynamically aligns prediction horizons with sequence predictability using entropy-based segmentation, consistently outperforming standard MTP on math, code, and general benchmarks across three LLM backbones.
Andrej Karpathy released nanochat, a minimalist LLM training framework that can train a GPT-2-level model for just around $48, covering the entire pipeline of pretraining, fine-tuning, and reinforcement learning, with minimal and fully transparent code.
The Qwen team proposes the Skill Self-Play framework, which significantly improves model capabilities on tool-calling and reasoning tasks through the collaboration of Proposer, Solver, and a dynamic skill controller in self-play.
The paper introduces Skill Self-Play (Skill-SP), a co-evolutionary framework that uses a proposer, solver, and skill controller to bridge structured verification and open-ended exploration, improving LLM performance on tool-use and reasoning benchmarks.
This paper introduces Experiential Learning (EL), a method that repurposes an LLM-as-a-Judge into an LLM-as-a-Coach to provide rich textual feedback instead of scalar rewards, improving performance and generalization on open-ended non-verifiable tasks.
Andrej Karpathy frames LLM training as text, conversations, and environments; Prime Intellect's Verifiers is an open-source framework for building and sharing RL environments for LLMs, released under MIT license, with a hub of 2500+ environments.
This paper from Tsinghua University introduces Single-rollout Asynchronous Optimization (SAO) for reinforcement learning post-training of LLMs. SAO replaces batch-based GRPO with single-rollout asynchronous training to reduce idle GPU time and improve stability, and it was used to train the GLM-5.2 model (750B-A40B), achieving state-of-the-art results on agentic coding and reasoning benchmarks.
The author details training an LLM from scratch on 160GB of 1800s English texts, with a 500M parameter evaluation model and plans for a 2B model, showing promising results for historical Q&A.
Chris Siebenmann explains his anti-crawler measures that block old browsers due to a surge in high-volume crawlers collecting data for LLM training, causing confusion for feed readers and archival services.
A tweet shares how training an LLM from scratch helped the author stand out in AI interviews and answer questions on Transformers, fine-tuning, and RAG confidently.
A technical tutorial on building a reinforcement learning environment for LLMs using the open-source Verifiers library, with Othello as a working example.
Thom Wolf describes a weekend project where AI agents collaboratively build a living wiki on reinforcement learning for LLM training, and the event logs are visualized as an isometric town to monitor activity.
SCAPE is a communication-efficient distributed optimizer that leverages first-moment statistics to enable extreme sparsification for LLM training, preserving accuracy while reducing wall-clock time by up to 43.3%.
DeadPool introduces a fault-tolerance mechanism for LLM training that enables hot-swapping of failed nodes with spare nodes using zero-overhead in-memory checkpointing, achieving fast recovery without interrupting the job.
CausalMix formulates data mixture optimization as a causal inference problem for LLM training, enabling dynamic adaptation to shifting data distributions without costly retraining, and demonstrates improved performance on Qwen2.5-0.5B and Qwen3-4B-Base.
Snowflake AI Research releases Arctic RL, an open-source unified RL backend that integrates with VeRL and SkyRL, enabling up to 6x actor-update acceleration and 3.5x end-to-end training speedup. It includes recipes for text-to-SQL and multi-hop QA, achieving competitive accuracy on enterprise benchmarks.
DataStates-LLM introduces a scalable checkpointing architecture for transformer models using composable state providers, achieving up to 4x higher throughput and reducing training time by 2.2x compared to existing solutions.