What are RLVR environments for LLMs? | Policy - Rollouts - Rubrics

🦋 check out prime intellect's envrionment hub to publish, explore and use RL environment: https://app.primeintellect.ai/dashboa...

Reinforcement learning is becoming the defining ingredient behind the most capable AI agents. From OpenAI’s Deep Research to Anthropic’s Claude Code, RL is used to specialize models for reasoning, coding, and tool use.

In this video we'll do a beginner friendly overview of Reinforcement Learning with Verifiable Rewards (RLVR) environment and how to build them using the verifiers library!

📌 also, if you are a beginner: learn to code from full-stack to AI with Scrimba https://scrimba.com/?via=yacineMahdid (extra 20% off pro with my link, great resource, I love the team)

Table of Content
00:00 - Introduction: RL’s growing role in agentic AI
01:10 - The RLVR loop: dataset, policy, rollouts, rewards, updates
02:13 - Overview of the state of RLVR
03:50 - Small-model RLVR: performance, latency, and cost benefits
06:00 - RLVR vs RLHF: key conceptual differences
07:32 - Open-source frameworks: ReasoningGym, ART, TRL and Verifiers
08:12 - deep dive into the verifiers 7 steps with math-python env
08:25 - deep dive into the verifiers | step 1 : data
09:09 - deep dive into the verifiers | step 2 : interaction style
09:40 - deep dive into the verifiers | step 3 : environment logic
10:05 - deep dive into the verifiers | step 4 : rewards function (rubric)
11:23 - deep dive into the verifiers | step 5 : parser (optional)
11:46 - deep dive into the verifiers | step 6 : package environment
12:07 - deep dive into the verifiers | step 7 : run eval or training
12:30 - a few community environments
13:25 - Case study: Building a Vision-Language RLVR environment feat alexine
13:56 - vision SR1 - overview
16:46 - vision SR1 - environment 1
18:29 - vision SR1 - environment 2
20:03 - Interview with prime Will Brown, creator of Verifiers
20:18 - Interview with prime Will Brown - verifiers development story
23:16 - Interview with prime Will Brown - what's the vision for environment hub?
24:17 - Interview with prime Will Brown - what future is there for RL environment?
26:27 - 👺🦋👺🦋👺🦋

Shout Out
👺 big thanks for alexine for her envrionment and for hoping on the video, check her out folks: https://x.com/alexinexxx
👺 thanks will for taking the time to have come down from gpu heaven to chat with us about verifiers: https://x.com/willccbb

Community Environment:
📌 MLE Bench Environment by C: https://app.primeintellect.ai/dashboa...
📌 Ifeval-confusables by oso: https://app.primeintellect.ai/dashboa...
📌 MAPP - Multi-Agent Path Planning Environment by salty duck: https://app.primeintellect.ai/dashboa...
📌 Vision SR1 by ma gurl alexine: https://app.primeintellect.ai/dashboa...


Paper & Videos & Cool Links:
📌 OpenAI’s Deep Research Team on Why Reinforcement Learning is the Future for AI Agents:    • OpenAI’s Deep Research Team on Why Reinfor...  
📌 Reinforcement Learning Meets Large Language Models: A Survey of Advancements and Applications Across the LLM Lifecycle: https://arxiv.org/abs/2509.16679v1
📌 Exploring Environments Hub: Your Language Model needs better (open) environments to learn: https://huggingface.co/blog/anakin87/...
📌 How to Train Your Agent: Building Reliable Agents with RL — Kyle Corbitt, OpenPipe:    • How to Train Your Agent: Building Reliable...  
📌 ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models: https://arxiv.org/abs/2505.24864v1
📌 reasoning gym library: https://github.com/open-thought/reaso...
📌 ART library: https://github.com/OpenPipe/ART
📌 huggingface TRL: https://github.com/huggingface/trl
📌 verifiers library: https://github.com/PrimeIntellect-ai/...

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