1+ day, 9+ 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…...

1+ mon, 13+ 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…...

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…...

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…...

3+ mon, 2+ week ago   (935+ words) In this tutorial, we explore the TuringEnterprises/Open-MM-RL dataset as a practical foundation for multimodal reasoning and reinforcement learning with verifiable rewards. We load the dataset, inspect its schema, analyze domains, formats, question lengths, answer types, and image distributions, and…...

4+ mon, 1+ week ago   (555+ words) In this tutorial, we walk through an end-to-end implementation of an advanced machine learning pipeline using ZenML. We begin by setting up the environment and initializing a ZenML project, then define a custom materializer that enables seamless serialization and metadata…...

9+ mon, 1+ week ago   (309+ words) In this tutorial, we build an advanced meta-cognitive control agent that learns how to regulate its own depth of thinking. We treat reasoning as a spectrum, ranging from fast heuristics to deep chain-of-thought to precise tool-like solving, and we train…...

9+ mon, 1+ week ago   (357+ words) Both DeepSeek-V3.2 and DeepSeek-V3.2-Speciale use the DeepSeek-V3 Mixture of Experts transformer with about 671B total parameters and 37B active parameters per token, inherited from V3.1 Terminus. The only structural change is DeepSeek Sparse Attention, introduced through continued pre-training. DeepSeek Sparse Attention splits…...

9+ mon, 3+ week ago   (254+ words) We set up the core structure of our deep reinforcement learning system. We initialize the environment, create the dueling Q-network, and prepare the replay buffer to store transitions efficiently. As we establish these foundations, we prepare everything our agent needs…...

10+ mon, 1+ week ago   (278+ words) We begin by setting up the environment and defining the symbolic tools our agent can use. We create a small synthetic world where each action, such as multiplication, addition, or subtraction, acts as an internal tool. This environment enables us…...