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DeepSeek-V3.2 rivals Gemini 3.0-Pro with 3 breakthrough innovations: DSA sparse attention, scalable RL framework, and 85K+ agent training tasks. Compare V3.2 vs Speciale for your use case.

OpenAI co-founder Ilya Sutskever declares the 'Age of Scaling' is over in exclusive interview. Discover why pre-training limits are here, what's next for AI research, and SSI's mission for safe superintelligence.

xAI launches Grok 4.1 with 2M context window, 3x lower hallucination rate, EQ-Bench3 #1 ranking, and ultra-affordable API pricing at $0.20 input/$0.50 output per 1M tokens. Full performance breakdown & pricing guide.

Explore Kimi K2, the 1.04T parameter open-source MoE model. Our deep dive covers its MuonClip optimizer, agentic AI training, and benchmark performance.

Explore Jason Wei's three laws of AI: the Verifier's Law, Commoditization of Intelligence, and the Jagged Edge. A framework for understanding AI's future progress and automation timeline.

Master distributed RLHF frameworks: Compare OpenRLHF and veRL architectures. Learn Ray Actors, GPU colocation, PPO implementation, and hybrid engine design for scalable reinforcement learning systems.

We tested 10 multi-agent AI frameworks and ranked them by use case. CrewAI wins for production, OpenAI Swarm for learning, AutoGen for complex tasks. Benchmarks, code examples, and decision matrix included.

AI agent challenges explained: solve latency issues, fix brittle planning, and avoid reflection loops. Advanced engineering patterns and RL techniques for production-ready agentic AI systems.

Boost RAG accuracy by 70%: We tested 9 chunking strategies (fixed-size, recursive, semantic). Semantic chunking won. Optimal: 256-512 tokens, 10-20% overlap. Includes Python/LangChain code and production tips.

Complete visual guide to LLM agents. Explore 5 types of AI agents: single-agent, multi-agent, ReAct, autonomous loops. With architecture diagrams & real examples.

Learn how GRPO-RoC fixes outcome-based reward issues. This training method improves AI reasoning by 40% through data curation. With code examples & benchmarks.

Discover how the Checkpoint-Engine achieves a 30x speedup in LLM RL training. Learn about its innovative approach to parameter updates for large-scale RL.

Build agentic RAG with LangGraph and Qwen achieving 5x better accuracy on complex queries. Learn agent-based retrieval, multi-step reasoning, self-correction. Complete code tutorial with real benchmarks.

Learn the complete Supervised Fine-Tuning (SFT) pipeline to enhance LLM reasoning. This guide covers the DeepSeek R1 process, from SFT to knowledge distillation.

Learn to implement a full GRPO training pipeline. This guide covers Supervised Fine-Tuning (SFT) with cold-start data, CoT prompting, and the GRPOTrainer.

Explore the 5 core reward functions powering DeepSeek-Coder-V2. Learn how its modular reward model for accuracy, reasoning, and format shapes AI behavior.

Learn to replicate the DeepSeek R1 training process. This guide covers building a reinforcement learning pipeline from scratch using GRPO for advanced LLM reasoning.

Explore the shift to separated architectures for RL post-training of LLMs. Learn how systems like AsyncFlow & TransferQueue solve data orchestration challenges.

Discover a critical flaw in Supervised Fine-Tuning (SFT) that limits LLM performance. Learn how a simple learning rate tweak unifies SFT and DPO for a 25% gain.

Traditional reinforcement learning models struggle with real-time applications due to "AI lag." Two ICLR 2025 papers from Mila introduce groundbreaking solutions to tackle inaction and delay regret, enabling large AI models to operate in high-frequency, dynamic environments without compromising speed or intelligence.

Build production agentic AI with 7 critical infrastructure components: MicroVM runtime, memory service, zero-trust security, tool gateway. Complete AWS Bedrock AgentCore setup guide. Learn session isolation, S3 vector storage, 8-hour workflows.

Compare DeepSeek V3 vs Llama 4 architecture: MLA vs GQA attention, MoE vs dense models. Learn how 671B parameters run at 37B speed. Includes code examples and design trade-offs.

# Baidu ERNIE 4.5: Advancements in Multimodal Large Language Models Baidu's ERNIE 4.5 marks a major leap in artificial intelligence, especially in the development of **multimodal large language mode...

Master Supervised Fine-Tuning (SFT) transforming base models to chat assistants. Complete 3-stage pipeline: base → instruct → chat model. LoRA reduces cost 70%, 7B model SFT in 2-4 hours on A100 ($10-20). Alpaca vs Dolly vs Open-Orca datasets compared.

Explore single-controller vs multi-controller in veRL, inspired by Google's Pathways, and learn their impact on distributed reinforcement learning systems.

The field of artificial intelligence has seen rapid advancements in reinforcement learning for reasoning, particularly within large language models (LLMs). This article reviews influential research s...

xAI's Grok 4 dominates AI benchmarks with 45% on Human Last Exam (Gemini: 21%), doubles ARC AGI scores, and introduces multi-agent architecture. Trained on 200K GPU Colossus supercomputer. Full performance breakdown.

## Qwen3 Training Pipeline: Pre-training, Reinforcement Learning, and Model Distillation ### Qwen3 Pre-training: Building a Robust Foundation Qwen3 training begins with a comprehensive three-stage ...

Master training 671B parameter LLMs with RL. Solve 5 critical challenges: Megatron vs FSDP, memory offloading, weight conversion, 1000+ GPU scaling. Real DeepSeek V3 workflow with GRPO achieving 10x speedup.

SGLang crushes vLLM with 3x throughput and 40% cost savings via prefill-decode separation. Real H800/A100 benchmarks, architecture deep-dive, production deployment guide. The future of LLM inference.

### Why Direct Reinforcement Learning on Base Language Models is the Next Frontier Direct reinforcement learning (RL) on base language models is emerging as a transformative approach in LLM optimiza...

Reinforcement learning for LLMs (large language models) is revolutionizing the field of artificial intelligence by enabling models to learn beyond the constraints of supervised learning. This article...

## Kimi Researcher: Advancing AI Agents with End-to-End Reinforcement Learning Kimi Researcher is the flagship product of the Kimi Agent initiative, designed to revolutionize research automation thr...