#LLM training

'Microsoft open sourced Agent Lightning to convert agent execution traces into RL transitions, enabling training of LLM policies with minimal integration and support for standard RL trainers.'

'Andrej Karpathy open-sourced nanochat — a compact end-to-end ChatGPT-style pipeline that runs a full tokenization-to-web-UI speedrun in ~4 hours on an 8×H100 node for roughly $100.'

'ZenFlow is a DeepSpeed offloading engine that removes CPU-induced GPU stalls using importance-aware pipelining, enabling up to 5× faster LLM training with minimal config changes.'

AI is advancing through self-improvement strategies like enhanced coding assistance, infrastructure optimization, and autonomous research, potentially accelerating the path to superintelligence.

AREAL is a new asynchronous reinforcement learning system that significantly speeds up training of large reasoning models by separating generation and training processes, achieving up to 2.77× faster training without loss of accuracy.

Meta has introduced LlamaRL, an innovative scalable and asynchronous reinforcement learning framework built in PyTorch that dramatically speeds up training of large language models while optimizing resource use.

Shanghai AI Lab researchers propose entropy-based scaling laws and novel techniques to overcome exploration collapse in reinforcement learning for reasoning-centric large language models, achieving significant performance improvements.

This guide explains how to fine-tune the Qwen3-14B model efficiently on Google Colab with Unsloth AI, leveraging 4-bit quantization and LoRA for memory-efficient training using mixed reasoning and instruction datasets.

Tsinghua University and ModelBest released Ultra-FineWeb, a trillion-token multilingual dataset that significantly improves large language model accuracy through innovative data filtering.

Tsinghua University researchers developed the Absolute Zero paradigm to train large language models without external data, using a self-evolving code executor system to enhance AI reasoning and learning.