Browsing: training

Tencent has released AngelSpec, an open-source, torch-native training framework for speculative-decoding draft models. The release covers both autoregressive multi-token prediction (MTP)…

Moonshot AI’s Kimi team and kvcache-ai have open-sourced AgentENV (AENV), a distributed platform for running agent environments at scale. AgentENV powers agentic reinforcement learning (RL) training…

A small group of AI researchers (Reactor) have released Open Dreamer, an open implementation of the Dreamer 4 world-model pipeline written in JAX and Flax NNX.…

Prime Intellect launched verifiers 0.2.0. It previews a rewritten core, shipped under the new verifiers.v1 namespace. Modern evaluations now run coding agents with tools, compaction, and…

Agentic LLMs often fail the same way, again and again. A Stanford research team traced this to missing, reusable capabilities. Their system, TRACE, diagnoses those gaps…

is straightforward. You load the weights, load the data, and wait for it to finish. For most models, that’s fine, and the only real cost is…

import importlib.util, os, shutil as _sh if importlib.util.find_spec(“tunix”) is None: print(“Installing Tunix + JAX ecosystem — this takes ~5-8 min…”) %pip install -q ipywidgets tensorboardX transformers…

rprint(Panel.fit(“[bold]Baseline 1: Predict output_type from context using pure Python Naive Bayes[/bold]”)) model_artifacts = {} classifier_df = df.dropna(subset=[“output_type”]).copy() classifier_df = classifier_df[ classifier_df[“output_type”].astype(str).str.len() > 0 ].copy() if classifier_df[“output_type”].nunique()…

A new hospital-style training ward has opened to give aspiring healthcare workers the chance to learn in a setting which feels like the real thing.Exeter College…

learning, the biggest bottleneck is almost never GPU memory or model size. It’s the handful of field samples you have access to across a vast, expensive,…