Walkinglabs Launches Comprehensive Reinforcement Learning Curriculum and Codebase

Walkinglabs Releases Hands-on Modern RL Course and Codebase

Walkinglabs just released a major update to its open-source reinforcement learning repository, according to the project's GitHub page — adding online training environments and hands-on labs for agentic RL and geometry reasoning using Vision-Language Models. The timing matters: post-training methods like DPO, GRPO, and RLVR dominate recent papers, but few codebases walk cleanly from MDP basics to those alignment-style techniques. If you're building reasoning or agent systems on top of RL, this release gives us one of the more grounded end-to-end references we've seen.

What's actually inside the curriculum

The book starts where it should — CartPole and multi-armed bandits make states, actions, rewards, and policies observable before any equations land. From there it builds through Markov decision processes, value functions, Bellman equations, DQN, policy gradients, actor-critic methods, PPO, continuous control, and offline RL. The second half is what makes it useful for 2026 work: RLHF, DPO, GRPO, and RLVR are treated as extensions of the same sequential-decision framework, not isolated acronyms.

Environments expand accordingly — to tools, browsers, code repositories, vision, and audio. An action may be a passage of text, a function call, or a sequence of interface operations, and a reward can come from human preference, a rule-based verifier, or a process reward model.

Running it — and the CUDA gotcha

Walkinglabs is collaborating with ModelScope to host online training environments. A ModelScope Studio puts the experiment interface, runtime, and training entry point on one browser page, so you can start training without configuring a local environment first. Each Studio ships with a companion notebook under code/online-experiments that imports the same runtime, exposes experiment parameters, prints the full training log, plots checkpoint evaluations, and displays the learned-policy replay.

Here's the gotcha worth flagging up front: CPU entries run on a standard notebook instance, but experiments 03, 08, 10, 11, and 12 need a scheduled ModelScope xGPU Notebook and check CUDA before training. Miss that line and you'll hit a silent failure — sanity-check the device before you queue a long run.

What to watch before you dive in

Two practical notes. The maintainers flag that the course was created with AI assistance and hasn't been fully reviewed yet — so expect the occasional factual mistake or non-running cell, and treat it as a working draft rather than a finished textbook. Chapters still marked as under construction may contain errors; pull requests and issues are welcome. The team is also seeking GPU support for compute; if you can help, the contact is in the repository.