<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Verifiers on SoloSoft</title><link>https://www.solosoft.dev/tags/verifiers/</link><description>Recent content in Verifiers on SoloSoft</description><generator>Hugo</generator><language>en-us</language><lastBuildDate>Fri, 01 May 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://www.solosoft.dev/tags/verifiers/index.xml" rel="self" type="application/rss+xml"/><item><title>Verifiers: Modular RL Environment Library for Training LLM Agents</title><link>https://www.solosoft.dev/post/verifiers-rl-environments-2026/</link><pubDate>Fri, 01 May 2026 00:00:00 +0000</pubDate><guid>https://www.solosoft.dev/post/verifiers-rl-environments-2026/</guid><description>&lt;p&gt;Verifiers is a modular Python library developed by &lt;a href="https://github.com/PrimeIntellect-ai/verifiers"&gt;PrimeIntellect-ai&lt;/a&gt; that provides a comprehensive framework for creating reinforcement learning environments tailored to training LLM agents. Designed for researchers and practitioners working on RL-based LLM alignment and agent optimization, Verifiers offers a clean, composable API with components for parsing model outputs, evaluating responses against rubrics, computing rewards, and running GRPO-based training loops.&lt;/p&gt;
&lt;p&gt;The library addresses a growing need in the AI research community: as RL-based methods like GRPO, PPO, and rejection sampling become standard for LLM fine-tuning, researchers need standardized, reusable environment components rather than building training infrastructure from scratch for each experiment. Verifiers provides exactly this &amp;ndash; a modular toolkit where environments are assembled from interchangeable building blocks.&lt;/p&gt;</description></item></channel></rss>