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A practical guide to MoE post-training, covering the tradeoff between load balancing and task quality, why RL becomes unstable when routing changes across engines or policy versions, and how to choose EP versus ETP in large-scale deployments.

If OpenClaw cron jobs are not firing, the root cause is usually not “cron is broken” but a mismatch in where scheduling is configured, which gateway is actually running, or whether the real execution path was ever verified. This guide walks through the shortest path to diagnosis.

Use OpenClaw to turn Vercel deployment events into useful chat alerts instead of raw webhook noise. Trigger wakeups from webhooks, schedule follow-up checks with cron, and deliver production context to Telegram, Feishu, or your team’s preferred chat surface.

Connect OpenClaw to Telegram so your AI assistant can reply in DMs and groups, remember context, run scheduled workflows, and proactively send useful updates instead of waiting in another tab.

Context engineering turns prompt management into a runtime systems problem for AI agents, covering reversible offload, just-in-time retrieval, lossy but recoverable summarization, sub-agent isolation, and cache-stable request design.

Learn how to connect OpenClaw to Feishu or Lark so your AI assistant can chat in DMs, handle group mentions, remember context, and proactively run real workflows for your team.

Learn how to connect OpenClaw to Telegram so your AI assistant can message you proactively, monitor workflows, and handle real operational tasks from a chat surface you already use every day.

WideSeek-R1 reframes broad information seeking as an organizational problem, using a lead-agent-subagent MARL system to parallelize search and approach DeepSeek-R1-level performance with a 4B model.

A practical guide to policy entropy collapse in RLVR and GRPO, covering why PPO clipping drives entropy decay and how dynamic clipping schedules restore exploration.

PlugMem turns raw AI agent logs into reusable knowledge, improving long-term memory quality while lowering context cost across LongMemEval, HotpotQA, and WebArena.

Learn how advanced importance sampling techniques like GEPO and VESPO solve data staleness in off-policy reinforcement learning for stable and efficient training.

How Ling 2.5 replaces part of GQA with a 1:7 MLA + Lightning Attention design to improve long-context throughput, reduce KV cache cost, and keep training quality stable.

A practical LLM Reinforcement Learning guide covering REINFORCE to PPO/GRPO derivations, plus production engineering patterns like async rollouts, importance sampling, and token-stream stability.

After 6 months of LLM RL training failures and breakthroughs, I share battle-tested solutions for training collapse, GRPO instability, exploration bottlenecks, and why Thinking models need special handling. Practical fixes you can apply today.

Master Google Nano Banana Pro to create publication-ready academic diagrams. Learn our proven 2-step workflow combining LLMs with AI image generation for CVPR/NeurIPS-quality scientific illustrations.

Discover 12 battle-tested lessons from months of production RL training. Learn why stability trumps everything, how agentic RL differs from reasoning RL, and practical strategies to avoid reward hacking in LLM training pipelines.

Discover why file systems are making a comeback in AI agent architecture. Learn how AGFS's 'everything is a file' philosophy enables seamless multi-agent collaboration and simplifies LLM tool integration with Unix-style commands.

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.

Discover how AI inference engines evolved from edge-optimized CNNs to cloud-scale LLMs. Learn the key differences between vLLM, TensorRT-LLM, and traditional frameworks like MNN and TVM in this comprehensive 2025 guide.

Best AI models for 2025 and 2026 compared in one guide: GPT-5.1 vs Claude Opus 4.5 vs Gemini 3 Pro vs Claude Sonnet 4.5 vs Grok 4.1, with pricing, context windows, and use-case picks.

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 Google's Nano Banana Pro (Gemini 3 Pro Image), the new AI image generation model with perfect text rendering and character consistency. Find out where to use it.

Google Gemini 3 Pro tops LMSYS Arena with record 1501 Elo score and dominates GPT-5.1 on AGI-critical benchmarks including Humanity's Last Exam (37.5% vs 26.5%) and ARC-AGI (45.1%), while achieving 100% on AIME 2025 with code execution.

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.

Best multi-agent AI frameworks for 2025 and 2026 compared: CrewAI, AutoGen, LangGraph, OpenAI Agents, Swarm, MetaGPT, Dify, and more. See the fastest picks for beginners, prototyping, and production.

Master prompt engineering with this complete 2025 guide. Learn zero-shot, few-shot, and Chain-of-Thought techniques for ChatGPT, Claude, and Gemini. Includes 20+ ready-to-use templates.

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.

Step-by-step TensorRT-LLM tutorial: Deploy Llama 3/GPT models 3x faster on A100/H100. Includes Python setup, Docker configuration, KV Cache optimization, and benchmarks vs vLLM. Complete guide in 20 minutes.

Benchmarked 4 vector databases with 10M vectors: Milvus wins on cost ($500/mo vs $1,200), Pinecone on ease (zero-ops). Real latency tests, throughput metrics, scalability comparisons. Includes migration guide & Python code.

Boost RAG accuracy with chunking: 9 core strategies + 2026 upgrades (contextual retrieval, late chunking, cross-granularity). Practical defaults: 256-512 tokens, 10-20% overlap. Includes Python/LangChain code and production tips.

Complete LangChain ecosystem comparison: LangChain for building chains, LangGraph for complex agents, LangSmith for monitoring. Decision matrix, code examples, and use cases for each tool.

Learn how to add special tokens to LLMs during fine-tuning without causing catastrophic forgetting. Our guide covers smart initialization and PEFT/LoRA.

Master Context Engineering achieving 40-70% better LLM accuracy with 3-stage RAG pipeline: pre-retrieval data prep, in-retrieval optimization (hybrid search + re-ranking), pre-generation construction. Reduce hallucinations, improve reliability.

Discover the top 10 AI tools thriving in the underground economy. Based on real API data, we reveal the AI coding agents and role-playing apps developers use.

Explore the future of AI programming assistants. Learn about a local-first, secure AI coding tool that automates refactoring, testing, and deployment from your CLI.

Best RAG frameworks 2025 compared with real benchmarks: LangChain for fast prototyping, Haystack for production, LlamaIndex for ingestion, and RAGFlow for visual workflows. Includes latency, cost, and Python examples.

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

Master RAG evaluation: 7 critical metrics from Recall@K to answer faithfulness. Code examples, benchmarks, RAGAS framework. Reduce hallucinations, boost accuracy by 40%.

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.

DeepSeek V3 Multi-head Latent Attention (MLA) cuts KV cache 4-8x vs standard MHA. Learn low-rank compression, matrix absorption, prefill vs decode phases. Complete PyTorch implementation with tensor shapes.

Explore the architecture of Alibaba's Qwen3-Next, a powerful large language model. Learn about its Mixture of Experts (MoE) design and performance.

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 how to build a Llama-style MoE language model from scratch. This guide covers the Mixture of Experts architecture, tokenization, and model setup.

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.

Learn how Prefill-Decode separation in LLM serving boosts goodput by 4.48x. Discover DistServe, a new architecture that optimizes latency and meets strict SLOs.

Compress LLMs 10-100x smaller using knowledge distillation. Learn teacher-student training, temperature scaling (T=3-5), soft targets. DistilBERT case: 40% smaller, 60% faster, 97% accuracy. Complete tutorial.

AI agents fail without proper infrastructure: 80% effort should go to infra, not models. Build 6-component pipeline: data pipelines, vector DBs (Pinecone/Weaviate), model serving, orchestration, monitoring, security. Cut costs 40-70% with optimization.

Fine-tune LLaMA 3 with zero coding in 3 steps using LLaMA Factory WebUI. Save 80% GPU memory with QLoRA on RTX 3090/4090. Beginner-friendly tutorial with CUDA setup. Supports 100+ models.

What is a transformer model in AI? Learn the Transformer architecture, self-attention, encoder-decoder flow, and how Transformers power GPT, BERT, Claude, and modern LLMs with diagrams and examples.

Run Llama 3, Mistral, CodeLlama locally with Ollama: 5-10x speedup on GPU, $0 API costs, complete privacy. One-command install on macOS/Linux/Windows. 8GB RAM minimum for 7B models, 16GB for 70B. Complete setup guide.

Comprehensive guide to Large Language Models: how LLMs work, Transformer architecture, training process, prompt engineering, and real-world applications. Learn about GPT, Claude, Gemini, and more.

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

Explore LLM inference optimization on H800 SuperPods. Learn how a disaggregated architecture with SGLang tackles the prefill bottleneck to boost throughput.

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.

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...

# MemOS: Persistent Memory for LLMs & Next-Gen AI Agents ![MemOS hero image showing a brain with digital connections](/images/2025/07-july/2025-07-17-memos-persistent-memory-llms-next-gen-ai_102.web...

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.

Google dominates 2025 LLM API market with 42% share vs OpenAI 28%. Gemini Flash is 10x cheaper ($0.075 vs $0.50/1M tokens). OpenRouter data reveals shocking shift. Full cost comparison + market trends.

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.

Migrating from traditional to AI infrastructure? Master 5 critical differences: GPU vs CPU scaling, KV Cache vs web caching, 3D parallelism vs load balancing. Real migration strategies for LLM systems in 2025.

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...

Compare 7 LLM sampling methods: Top-P (Nucleus), Temperature, Beam Search, Min-P, Mirostat. Fix repetitive outputs, improve quality. Includes parameter tuning guide for GPT/Claude/Gemini.

## 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...

Fix Qwen3 FP16 overflow on mobile devices: QK-Norm explained with code examples. Deploy LLMs on edge hardware (RTX 3060, mobile chips) with 90% error reduction.