<?xml version="1.0" encoding="UTF-8"?>
<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom">
  <channel>
    <title>g factor technologies Engineering Blog</title>
    <link>https://www.g-ftech.com/blog</link>
    <description>Deep-dive systems research, post-training architectures, and hardware benchmarks from g factor technologies.</description>
    <language>en-US</language>
    <lastBuildDate>Fri, 11 Sep 2026 18:01:44 GMT</lastBuildDate>
    <atom:link href="https://www.g-ftech.com/feed.xml" rel="self" type="application/rss+xml"/>
    <item>
      <title><![CDATA[We Tried ISO-AdamW. AdamW Kept Its Job.]]></title>
      <link>https://www.g-ftech.com/blog/iso-adamw-vs-adamw-grpo-experiment</link>
      <guid isPermaLink="true">https://www.g-ftech.com/blog/iso-adamw-vs-adamw-grpo-experiment</guid>
      <description><![CDATA[Our small GRPO experiment: ISO-AdamW scored 75.8% versus AdamW's 75.4%, with more GPU memory. The idea, the engineering detour, and what four answers mean.]]></description>
      <pubDate>Fri, 11 Sep 2026 00:00:00 GMT</pubDate>
      <author>corporate@g-ftech.com (g factor engineering)</author>
      <category>ISO-AdamW</category>
      <category>GRPO</category>
      <category>Experiments</category>
      <category>AdamW</category>
      <category>GSM8K</category>
      <category>Qwen3</category>
    </item>
    <item>
      <title><![CDATA[Benchmarking Qwen 3.8 27B Across Inference Providers: Together, Fireworks, Nebius, and g factor]]></title>
      <link>https://www.g-ftech.com/blog/qwen-27b-inference-providers-benchmark</link>
      <guid isPermaLink="true">https://www.g-ftech.com/blog/qwen-27b-inference-providers-benchmark</guid>
      <description><![CDATA[Qwen 3.8 27B throughput from concurrency 1 to 64: Together, Fireworks FP8, Doubleword, vanilla vLLM, and g factor MTP4, with latency and repeat counts.]]></description>
      <pubDate>Tue, 08 Sep 2026 00:00:00 GMT</pubDate>
      <author>corporate@g-ftech.com (g factor engineering)</author>
      <category>Inference</category>
      <category>vLLM</category>
      <category>H100</category>
      <category>B200</category>
      <category>Qwen 3.8 27B</category>
      <category>Together AI</category>
      <category>Fireworks AI</category>
      <category>FP8</category>
      <category>Benchmarks</category>
    </item>
    <item>
      <title><![CDATA[The Limits of AI: Induction, Deduction, and Why Models Can't Jump]]></title>
      <link>https://www.g-ftech.com/blog/ai-reasoning-limits-induction-deduction-abduction</link>
      <guid isPermaLink="true">https://www.g-ftech.com/blog/ai-reasoning-limits-induction-deduction-abduction</guid>
      <description><![CDATA[Why LLMs master pattern recognition (induction) and logical proof (deduction) but fail at hypothesis invention (abduction). Insights from DeepMind's Tom Zahavy.]]></description>
      <pubDate>Tue, 08 Sep 2026 00:00:00 GMT</pubDate>
      <author>corporate@g-ftech.com (g factor engineering)</author>
      <category>AI Limits</category>
      <category>Abduction</category>
      <category>Induction vs Deduction</category>
      <category>DeepMind</category>
      <category>Epistemology</category>
      <category>Reasoning</category>
      <category>World Models</category>
      <category>Frontier AI</category>
    </item>
    <item>
      <title><![CDATA[Physical AI: Why the Next Big Frontier Is Giving Software Agents Hands]]></title>
      <link>https://www.g-ftech.com/blog/physical-ai-embodied-agents-mhs-robotics</link>
      <guid isPermaLink="true">https://www.g-ftech.com/blog/physical-ai-embodied-agents-mhs-robotics</guid>
      <description><![CDATA[Why Physical AI is the natural next step for reasoning models: long-horizon tool loops, hardware as stateful APIs, Anthropic's MHS, OpenAI's humanoid push, and Isaac Lab gear assembly.]]></description>
      <pubDate>Tue, 08 Sep 2026 00:00:00 GMT</pubDate>
      <author>corporate@g-ftech.com (g factor engineering)</author>
      <category>Physical AI</category>
      <category>Robotics</category>
      <category>Embodied AI</category>
      <category>MHS</category>
      <category>MCP</category>
      <category>OpenAI</category>
      <category>Anthropic</category>
      <category>RLVR</category>
      <category>OpenEnv</category>
    </item>
    <item>
      <title><![CDATA[Distributed Training & Inference: From CPUs and GPUs to a Cluster]]></title>
      <link>https://www.g-ftech.com/blog/distributed-training-inference-cpu-gpu-clusters</link>
      <guid isPermaLink="true">https://www.g-ftech.com/blog/distributed-training-inference-cpu-gpu-clusters</guid>
      <description><![CDATA[An illustrated guide to CPUs, GPUs, CUDA, clusters, data, tensor and pipeline parallelism, FSDP, DeepSpeed ZeRO, Ray, and inference.]]></description>
      <pubDate>Tue, 08 Sep 2026 00:00:00 GMT</pubDate>
      <author>corporate@g-ftech.com (g factor engineering)</author>
      <category>Distributed Training</category>
      <category>Inference</category>
      <category>CUDA</category>
      <category>FSDP</category>
      <category>DeepSpeed</category>
      <category>Ray</category>
    </item>
    <item>
      <title><![CDATA[From RLHF to RLVR: The Evolution of Reward Signals and the Battle Against Reward Hacking]]></title>
      <link>https://www.g-ftech.com/blog/rlhf-to-rlvr-reward-hacking</link>
      <guid isPermaLink="true">https://www.g-ftech.com/blog/rlhf-to-rlvr-reward-hacking</guid>
      <description><![CDATA[The journey from human preference (RLHF) and LLM-as-a-judge to verifiable rewards (RLVR). Why soft evaluators collapse and how to design tamper-proof verifiers.]]></description>
      <pubDate>Tue, 08 Sep 2026 00:00:00 GMT</pubDate>
      <author>corporate@g-ftech.com (g factor engineering)</author>
      <category>RLVR</category>
      <category>RLHF</category>
      <category>Reward Hacking</category>
      <category>Lean 4</category>
      <category>Verifiable Rewards</category>
      <category>LLM-as-a-Judge</category>
      <category>Post-Training</category>
      <category>Reinforcement Learning</category>
    </item>
    <item>
      <title><![CDATA[AsyncGRPO: Eliminating GPU Idle Bubbles in Environment-Heavy RL Post-Training]]></title>
      <link>https://www.g-ftech.com/blog/async-grpo-gym-rollout-scaling</link>
      <guid isPermaLink="true">https://www.g-ftech.com/blog/async-grpo-gym-rollout-scaling</guid>
      <description><![CDATA[How AsyncGRPO decouples rollout generation, CPU gym execution, and policy updates to eliminate 80% GPU idle bubbles in environment-heavy agent reinforcement learning.]]></description>
      <pubDate>Tue, 08 Sep 2026 00:00:00 GMT</pubDate>
      <author>corporate@g-ftech.com (g factor engineering)</author>
      <category>AsyncGRPO</category>
      <category>Reinforcement Learning</category>
      <category>Gym Environments</category>
      <category>GPU Utilization</category>
      <category>Data Locality</category>
      <category>vLLM</category>
      <category>H100 / H200</category>
      <category>Post-Training</category>
    </item>
    <item>
      <title><![CDATA[Owning vs. Renting Intelligence: Why Enterprises Are Building Sovereign AI]]></title>
      <link>https://www.g-ftech.com/blog/owning-vs-renting-intelligence</link>
      <guid isPermaLink="true">https://www.g-ftech.com/blog/owning-vs-renting-intelligence</guid>
      <description><![CDATA[Why relying solely on frontier APIs is a strategic risk for sensitive industries. The economics, data privacy, compliance, and post-training mechanics of owned AI.]]></description>
      <pubDate>Tue, 08 Sep 2026 00:00:00 GMT</pubDate>
      <author>corporate@g-ftech.com (g factor engineering)</author>
      <category>Owned Intelligence</category>
      <category>Enterprise AI</category>
      <category>Data Sovereignty</category>
      <category>Post-Training</category>
      <category>GRPO</category>
      <category>SFT</category>
      <category>AI Economics</category>
      <category>Open Models</category>
    </item>
    <item>
      <title><![CDATA[LoRA & DoRA: The Math, Memory, and Trade-offs]]></title>
      <link>https://www.g-ftech.com/blog/lora-dora-parameter-efficient-fine-tuning</link>
      <guid isPermaLink="true">https://www.g-ftech.com/blog/lora-dora-parameter-efficient-fine-tuning</guid>
      <description><![CDATA[Understand LoRA and DoRA with typeset formulas, interactive matrix dimensions, and explicit memory calculations. Includes QLoRA and sourced benchmark results.]]></description>
      <pubDate>Tue, 08 Sep 2026 00:00:00 GMT</pubDate>
      <author>corporate@g-ftech.com (g factor engineering)</author>
      <category>LoRA</category>
      <category>DoRA</category>
      <category>QLoRA</category>
      <category>PEFT</category>
      <category>Memory Optimization</category>
      <category>Fine-Tuning</category>
      <category>ICML 2024</category>
      <category>VRAM Math</category>
    </item>
    <item>
      <title><![CDATA[Multi-Reward Reinforcement Learning for LLM Agents: Comparing PPO, GRPO, DAPO, and GDPO]]></title>
      <link>https://www.g-ftech.com/blog/multi-reward-rl-ppo-grpo-dapo-gdpo</link>
      <guid isPermaLink="true">https://www.g-ftech.com/blog/multi-reward-rl-ppo-grpo-dapo-gdpo</guid>
      <description><![CDATA[A technical comparison of PPO, GRPO, DAPO, and GDPO in multi-reward agent post-training: normalization math, scale dominance, reward collapse, and benchmarks.]]></description>
      <pubDate>Tue, 08 Sep 2026 00:00:00 GMT</pubDate>
      <author>corporate@g-ftech.com (g factor engineering)</author>
      <category>Multi-Reward RL</category>
      <category>GDPO</category>
      <category>GRPO</category>
      <category>DAPO</category>
      <category>PPO</category>
      <category>Reward Normalization</category>
      <category>AI Agents</category>
    </item>
    <item>
      <title><![CDATA[SFT vs. RL: What Changes Inside the Model?]]></title>
      <link>https://www.g-ftech.com/blog/sft-vs-rl-spectral-reasoning</link>
      <guid isPermaLink="true">https://www.g-ftech.com/blog/sft-vs-rl-spectral-reasoning</guid>
      <description><![CDATA[An intuitive guide to SFT, RLVR, and weight spectra. Explore SVD in 3D, understand the ISO paper’s evidence, and separate training-step gains from runtime savings.]]></description>
      <pubDate>Tue, 08 Sep 2026 00:00:00 GMT</pubDate>
      <author>corporate@g-ftech.com (g factor engineering)</author>
      <category>SFT vs RL</category>
      <category>Reinforcement Learning</category>
      <category>RLVR</category>
      <category>Spectral Analysis</category>
      <category>SVD Decomposition</category>
      <category>Singular Frames</category>
      <category>Isospectral Optimization</category>
    </item>
    <item>
      <title><![CDATA[Latent-GRPO: Reinforcement Learning in Continuous Thought Space]]></title>
      <link>https://www.g-ftech.com/blog/latent-grpo-deep-dive</link>
      <guid isPermaLink="true">https://www.g-ftech.com/blog/latent-grpo-deep-dive</guid>
      <description><![CDATA[Why think in words? A deep dive into Latent-GRPO, continuous thought recurrence (Coconut, SofT-GRPO, CoLaR, SLPO), policy gradients over continuous embeddings, and empirical benchmarks on Qwen3.6-27B.]]></description>
      <pubDate>Tue, 08 Sep 2026 00:00:00 GMT</pubDate>
      <author>corporate@g-ftech.com (g factor engineering)</author>
      <category>Latent Thinking</category>
      <category>GRPO</category>
      <category>Continuous Reasoning</category>
      <category>Coconut</category>
      <category>SofT-GRPO</category>
      <category>CoLaR</category>
      <category>Qwen3.6-27B</category>
      <category>Reinforcement Learning</category>
    </item>
    <item>
      <title><![CDATA[High-Throughput LLM Inference & Training: A Deep Dive into vLLM]]></title>
      <link>https://www.g-ftech.com/blog/vllm-throughput-deep-dive</link>
      <guid isPermaLink="true">https://www.g-ftech.com/blog/vllm-throughput-deep-dive</guid>
      <description><![CDATA[A technical deep dive into vLLM: PagedAttention, continuous batching, chunked prefill, CUDA graphs, and empirical benchmarks from gft-studio across GRPO rollout phases and inference serving on NVIDIA H100 and H200.]]></description>
      <pubDate>Tue, 08 Sep 2026 00:00:00 GMT</pubDate>
      <author>corporate@g-ftech.com (g factor engineering)</author>
      <category>vLLM</category>
      <category>PagedAttention</category>
      <category>Continuous Batching</category>
      <category>CUDA Graphs</category>
      <category>Inference Optimization</category>
      <category>FP8</category>
    </item>
  </channel>
</rss>