Breaking the Agent Training Bottleneck: Open‑Source ClawGym Data, Training, and Evaluation Pipeline

ClawGym provides a complete open‑source framework for Claw‑style personal agents, linking a 13.5 K synthetic task dataset, black‑box rollout training, sandbox‑parallel reinforcement learning, and a rigorously verified benchmark of 200 tasks, and demonstrates that synthetic data can lift a 30 B model beyond a 235 B baseline.

ClawGymOpenClawagent training

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Breaking the Agent Training Bottleneck: Open‑Source ClawGym Data, Training, and Evaluation Pipeline

Machine Learning Algorithms & Natural Language Processing

Apr 25, 2026 · Artificial Intelligence

Why DeepSeek‑V4 Took Twice as Long: Inside the Training‑Stability Challenges and Engineering Hacks

The DeepSeek‑V4 technical report reveals that the model’s doubled training time stems from massive token and parameter scaling, severe training‑stability issues in MoE layers, and a suite of engineering solutions—including Anticipatory Routing, SwiGLU Clamping, specialist expert training, and a custom sandbox cluster—while also exposing high hallucination rates despite impressive benchmark performance.

DeepSeek V4Generative Reward ModelLLM

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Why DeepSeek‑V4 Took Twice as Long: Inside the Training‑Stability Challenges and Engineering Hacks

Baobao Algorithm Notes

Apr 14, 2026 · Industry Insights

Why Mastering AI Agents Is the Most Critical Skill Right Now

The article argues that leveraging AI agents like Claude Code is now the top priority for developers, explaining how agents boost productivity, the importance of their operating environment, and why embracing them is essential for future success in the AI-driven workplace.

Claude CodeEnvironmentLLM

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Why Mastering AI Agents Is the Most Critical Skill Right Now

Meituan Technology Team

Jan 29, 2026 · Artificial Intelligence

How LongCat‑Flash‑Thinking‑2601 Achieves Real‑World Generalization for Agents

LongCat‑Flash‑Thinking‑2601, a 560‑billion‑parameter MoE model, combines environment expansion, multi‑environment RL, systematic noise training, a heavy‑thinking reasoning mode, and Zigzag sparse attention to deliver strong benchmark performance and robust real‑world agent capabilities.

Environment ExpansionLarge Language ModelOpen Source

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How LongCat‑Flash‑Thinking‑2601 Achieves Real‑World Generalization for Agents