A Little Course on Reinforcement Learning Environments for LLMs
by Stefano Fiorucci (anakin87)
Build an RL environment from scratch and use it to train a 2.6B model into a competent Tic Tac Toe player.
Overview
Stefano Fiorucci (anakin87), an NLP engineer who has worked extensively on the Haystack framework, wrote this as a single Apache-2.0 GitHub repository that has drawn roughly 220 stars. The spine is one example carried end to end rather than a survey. Chapter 1 maps classic reinforcement learning vocabulary — agents, environments, rollouts, rewards — onto the language-model setting. Chapter 2 introduces Verifiers, the open-source library from Prime Intellect that treats an RL environment as a versioned software artifact. Chapter 3 implements a Tic Tac Toe environment against it. Chapter 4 then uses that same environment purely as an evaluation harness before any training happens, which is the step most RL write-ups skip and the reason the later numbers mean anything. Chapters 5 and 6 generate synthetic gameplay data and run a supervised fine-tuning warm-up on LiquidAI's LFM2-2.6B. Chapters 7 and 8 run the reinforcement learning itself, with vLLM serving rollouts. Chapter 9 is a post-mortem of the experiments that failed, and Chapter 10 covers where to go next. The published checkpoint reports its results honestly: 86% wins against a random opponent, 85% draws against optimal play, and 100% output-format compliance, with a noted weakness against fork traps.
At a Glance
- Topic
- Fine-Tuning
- Level
- Intermediate
- Format
- Course
- Cost
- Free
- Duration
- 10 chapters, self-paced — roughly a weekend to read through, considerably longer if you actually run the SFT and RL training
- Provider
- Stefano Fiorucci (anakin87)
- Hands-on
- Yes — code/exercises
- Certificate
- None
What You’ll Learn
- ✓Map reinforcement learning concepts like rollouts, rewards and policies onto language model training
- ✓Build a custom RL environment as a reusable software artifact with the Verifiers library
- ✓Write a verifiable reward function and a strict output format that a small model can hit
- ✓Use the same environment as an evaluation harness before spending any compute on training
- ✓Generate synthetic trajectories and run a supervised fine-tuning warm-up before reinforcement learning
- ✓Run group-based RL training on a 2.6B parameter model with vLLM serving the rollouts
- ✓Read a failed-experiment post-mortem to recognise the RL failure modes before hitting them yourself
Highlights
- •One example carried all the way to a published, evaluated checkpoint on the Hugging Face Hub — not a toy snippet
- •Includes a whole chapter of failed experiments, which is rarer and more useful than the successful path
- •Teaches evaluation with the environment before training with it, so the improvement claims are measurable
- •Uses Verifiers, the same environment library behind Prime Intellect's Environments Hub, rather than a bespoke teaching harness
- •Runs on a 2.6B model, so the whole loop is reproducible without a cluster
Who It’s For
Best For
- ✓AI engineers comfortable with supervised fine-tuning who have never built an RL environment
- ✓Traditional RL practitioners who want to apply their background to language models
- ✓Engineers evaluating whether RLVR-style post-training is worth it for their own task
Prerequisites
- •Working Python and PyTorch, plus familiarity with LLM fine-tuning basics such as LoRA or full SFT
- •Access to a GPU if you intend to run the training chapters rather than only read them
- •No prior reinforcement learning knowledge is assumed — Chapter 1 builds it from the ground up
FAQ
What is A Little Course on Reinforcement Learning Environments for LLMs?
A free ten-chapter course that teaches reinforcement learning for language models through one worked example carried all the way to a published checkpoint: building a Tic Tac Toe environment with the Verifiers library, using it first to evaluate models and then to train LiquidAI's LFM2-2.6B through supervised warm-up and RL. It is aimed at engineers who know LLM fine-tuning but have never built an RL environment, and at RL practitioners who have never applied it to a language model.
Is A Little Course on Reinforcement Learning Environments for LLMs free?
A Little Course on Reinforcement Learning Environments for LLMs is free to access.
What level is A Little Course on Reinforcement Learning Environments for LLMs for?
A Little Course on Reinforcement Learning Environments for LLMs is aimed at a intermediate audience. Recommended background: Working Python and PyTorch, plus familiarity with LLM fine-tuning basics such as LoRA or full SFT, Access to a GPU if you intend to run the training chapters rather than only read them, No prior reinforcement learning knowledge is assumed — Chapter 1 builds it from the ground up.
How long does A Little Course on Reinforcement Learning Environments for LLMs take?
Expect roughly 10 chapters, self-paced — roughly a weekend to read through, considerably longer if you actually run the SFT and RL training. Most learners work through it at their own pace.
What will I learn from A Little Course on Reinforcement Learning Environments for LLMs?
You'll learn: Map reinforcement learning concepts like rollouts, rewards and policies onto language model training; Build a custom RL environment as a reusable software artifact with the Verifiers library; Write a verifiable reward function and a strict output format that a small model can hit; Use the same environment as an evaluation harness before spending any compute on training; Generate synthetic trajectories and run a supervised fine-tuning warm-up before reinforcement learning; Run group-based RL training on a 2.6B parameter model with vLLM serving the rollouts; Read a failed-experiment post-mortem to recognise the RL failure modes before hitting them yourself.
Topics
Sources
This page was written from 3 sources, 2 on domains other than github.com.