4+ hour, 11+ 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…...

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…...

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…...

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…...

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…...

3+ mon, 1+ week ago   (962+ words) Most language models improve in discontinuous jumps. A team collects data, trains, and ships a new version. This takes months and produces remarkable or catastrophic behavior for users. Trajectory wants to replace that cycle with continual learning. The Trajectory team…...

3+ mon, 2+ week ago   (917+ words) Knowledge distillation (KD) transfers “dark knowledge” from a large teacher model to a smaller student. The student learns from the teacher’s full output probability distribution over tokens, not just correct answers. This is done via per-position Kullback–Leibler (KL) divergence…...

3+ mon, 2+ week ago   (876+ words) Large language models become static after pretraining. Their knowledge does not update as the world changes. Retraining a full LLM is too expensive at modern scales. Fine-tuning risks degrading previously learned knowledge. Retrieval-augmented generation (RAG) struggles when answers require reasoning…...

3+ mon, 2+ week ago   (628+ words) In this tutorial, we build an advanced federated learning experiment with NVIDIA FLARE. We compare FedAvg and FedProx on a non-IID CIFAR-10 setup, where client data is split using a Dirichlet distribution to simulate realistic label imbalance across federated sites....

3+ mon, 3+ week ago   (1602+ words) Lighthouse takes a different approach on both design decisions. It pools queries, keys, and values symmetrically across a multi-level pyramid, and it places selection entirely outside the attention kernel. After selection, the system gathers the chosen entries into a contiguous,…...