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Andrej Karpathy has open-sourced nanochat, a compact, dependency-light codebase that implements a full ChatGPT-style stack—from tokenizer training to web UI inference—aimed at reproducible, hackable LLM training on a single multi-GPU node. The repo provides a single-script “speedrun” that executes the full loop: tokenization, base pretraining, mid-training on chat/multiple-choice/tool-use data, Supervised Finetuning (SFT), optional RL on…

Do you actually need a giant VLM when dense Qwen3-VL 4B/8B (Instruct/Thinking) with FP8 runs in low VRAM yet retains 256K→1M context and the full capability surface? Alibaba’s Qwen team has expanded its multimodal lineup with dense Qwen3-VL models at 4B and 8B scales, each shipping in two task profiles—Instruct and Thinking—plus FP8-quantized checkpoints for…

How would your agent stack change if a policy could train purely from its own outcome-grounded rollouts—no rewards, no demos—yet beat imitation learning across eight benchmarks? Meta Superintelligence Labs propose ‘Early Experience‘, a reward-free training approach that improves policy learning in language agents without large human demonstration sets and without reinforcement learning (RL) in the…