<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Model Evaluation on The Coders Blog</title><link>https://thecodersblog.com/tag/model-evaluation/</link><description>Recent content in Model Evaluation on The Coders Blog</description><generator>Hugo</generator><language>en-us</language><lastBuildDate>Fri, 08 May 2026 16:18:05 +0000</lastBuildDate><atom:link href="https://thecodersblog.com/tag/model-evaluation/index.xml" rel="self" type="application/rss+xml"/><item><title>vLLM V1: Prioritizing Correctness in LLM Reinforcement Learning</title><link>https://thecodersblog.com/vllm-v0-to-v1-correctness-before-corrections-in-rl-2026/</link><pubDate>Fri, 08 May 2026 16:18:05 +0000</pubDate><guid>https://thecodersblog.com/vllm-v0-to-v1-correctness-before-corrections-in-rl-2026/</guid><description>&lt;p&gt;The quest for truly intelligent and reliable Large Language Models (LLMs) is a winding path, often paved with intricate engineering challenges. One such critical juncture lies in the domain of Reinforcement Learning (RL) for LLMs, where the devil is not just in the details, but in the very fabric of the training-inference loop. For researchers and engineers leveraging frameworks like PipelineRL, the transition from vLLM V0 to V1 represents not merely an incremental update, but a fundamental re-evaluation of priorities: correctness before corrections.&lt;/p&gt;</description></item></channel></rss>