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News
Implementation of Machine Learning Workflows with NVIDIA cuML, RAPIDS, GPU Benchmarking, Explainability, Clustering, and Model Inference
2+ hour, 52+ min ago (198+ words) We benchmark scikit-learn and cuML implementations of PCA, K-Means, nearest neighbors, logistic regression, random forests, and DBSCAN. We generate datasets on the GPU, synchronize CUDA operations for fair timing, and record the speedup each accelerated algorithm achieves. We also compare…...
Anthropic Adds Plugin Evals to Claude Code: 6 Grader Types, a No-Plugin Baseline, and a CI Gate for Skills
1+ day, 7+ hour ago (308+ words) Deployable: Yes. It runs on Claude Code v2.1.269 or later against any directory with a plugin.json or.claude-plugin/plugin.json manifest, or a skills-directory plugin. Every eval run and judge grader is a real model call billed to your plan…...
Hugging Face Unveils Microduck: A $399 Open-Source 25 cm Biped You Train with Reinforcement Learning
2+ week, 23+ hour ago (321+ words) Microduck is 25 cm tall, 14 cm wide, and under 800 g. It carries 15 motors across legs, neck, and head, plus an articulated beak that picks objects off the floor. Compute is a Rockchip RK3566 with an AI accelerator, 1 GB of RAM, and 32 GB of…...
Auditing Preference Biases and Fine-Tuning Language Models with Direct Preference Optimization on Anthropic HH-RLHF Using TRL and LoRA
3+ week, 2+ day ago (648+ words) This tutorial provides an end-to-end workflow for fine-tuning language models using Direct Preference Optimization (DPO). We demonstrate how to audit the Anthropic HH-RLHF dataset for structural and length-based biases, implement a robust training pipeline using TRL and LoRA, and evaluate…...
Create a Reasoning-Focused LLM: A Practical Guide to Streaming, Curating, and Fine-Tuning the SupraLabs Reasoning Corpus
4+ week, 2+ day ago (554+ words) MarkTechPost In this tutorial, we build an end-to-end workflow for working with the SupraLabs reasoning corpus. We stream a representative subset directly from the Hugging Face Hub, inspect its source distribution, token-length patterns, task composition, and reasoning-to-answer ratios, and then…...
AllenAI Open Instruct Tulu 3 Post-Training with SFT, DPO, RLVR, GRPO, and Verifier-Based Evaluation
1+ mon, 10+ hour ago (691+ words) Build a custom LLM post-training pipeline using AllenAI’s Open Instruct framework. This comprehensive guide walks through Supervised Fine-Tuning (SFT), Direct Preference Optimization (DPO), and Reinforcement Learning with Verifiable Rewards (GRPO), optimized to run efficiently on 16GB hardware without needing heavy distributed…...
Stanford Researchers Introduce TRACE: A Capability-Targeted Agentic Training System That Turns Recurrent Agent Failures Into Synthetic RL Environment
1+ mon, 4+ week ago (676+ words) 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 and trains for them directly. TRACE stands for Turning Recurrent Agent failures into Capability-targeted…...
Prime Intellect Releases prime-rl 0.6.0 to Train Trillion-Parameter MoE Models on Agentic RL Workloads
2+ mon, 2+ week ago (550+ words) The research team trained GLM-5 on SWE tasks at up to 131k sequence length. Step times stayed under five minutes. The batch size was 256 rollouts. The run used only 28 H200 nodes. prime-rl is an open framework for asynchronous reinforcement learning. It post-trains…...
VibeThinker-3B: A 3B Dense Reasoning Model Built on Qwen2.5-Coder-3B With the Spectrum-to-Signal Post-Training Pipeline
2+ mon, 3+ week ago (749+ words) While recent breakthroughs in AI reasoning have largely been driven by massive scale, pouring in billions of parameters to cross complex cognitive thresholds—VibeThinker-3B is charting a completely different path. Created by researchers from Sina Weibo Inc (China), this 3-billion-parameter…...
Meet Harness-1: A 20B Retrieval Subagent Trained With Reinforcement Learning Inside a Stateful Search Harness on gpt-oss-20b
3+ mon, 5+ day ago (671+ words) Harness-1 reaches 0.730 average curated recall across eight benchmarks, trailing only Opus-4.6 among the searchers tested. Their answer is Harness-1, a 20B retrieval subagent built on gpt-oss-20b. It was trained with reinforcement learning inside a stateful search harness. The harness holds the…...