Japan’s vast road network boasts 1.2 million kilometres of tarmac across its sprawling landscape. That might sound like a lot, but it manages some 82 million vehicles in some of the world’s most densely populated cities daily. As a country, it should be at a perpetual standstill. Yet, ever since the 1950s, the Japanese have…
ServiceNow Research has released DRBench, a benchmark and runnable environment to evaluate “deep research” agents on open-ended enterprise tasks that require synthesizing facts from both public web and private organizational data into properly cited reports. Unlike web-only testbeds, DRBench stages heterogeneous, enterprise-style workflows—files, emails, chat logs, and cloud storage—so agents must retrieve, filter, and attribute…
In all my years in Japan I’ve never seen an influx of tourists like I have in the last two years since the country opened up post-pandemic. And rightly so, there’s so much to see, visit, do and of course eat, oh and if you happen to be a car guy or gal, then you’re…
Tuning LLM outputs is largely a decoding problem: you shape the model’s next-token distribution with a handful of sampling controls—max tokens (caps response length under the model’s context limit), temperature (logit scaling for more/less randomness), top-p/nucleus and top-k (truncate the candidate set by probability mass or rank), frequency and presence penalties (discourage repetition or encourage…
NVIDIA AI has introduced Reinforcement Learning Pretraining (RLP), a training objective that injects reinforcement learning into the pretraining stage rather than deferring it to post-training. The core idea is simple and testable: treat a short chain-of-thought (CoT) as an action sampled before next-token prediction and reward it by the information gain it provides on the…
In this tutorial, we explore the advanced capabilities of PyTest, one of the most powerful testing frameworks in Python. We build a complete mini-project from scratch that demonstrates fixtures, markers, plugins, parameterization, and custom configuration. We focus on showing how PyTest can evolve from a simple test runner into a robust, extensible system for real-world…
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…
