RL in Production: From Foundations to LLM Post-Training
Dr. Rajat Dandekar
Vizuara Co-founder | Purdue PhD
Dr. Raj Dandekar
MIT PhD | Vizuara Co-founder
From Bellman equations to reasoning models, robotics and RL at scale.
Bring both phases of Vizuara’s RL in Production program into one learning path, taught by the Vizuara co-founders. Vizuara reaches an audience of 200,000+ followers.
Phase 1 follows the original seven lectures: RL foundations; Q-learning and DQN; policy gradients; TRPO and PPO; RLHF; GRPO; and DPO with agentic RL. Learn with the original slides, mathematical companions, visualizers and coding assignments.
Phase 2 examines six existing research projects: Socratic alignment, Dream to Catch, Dreaming to Dodge, robot grasping with SAC and RLPD, software-engineering RL, and veRL/PipelineRL training systems. Trace each project from its environment and reward to code, evaluation and limitations.
The RLVR Arena capstone trains a small language model on Countdown puzzles with GRPO and an exact arithmetic verifier. Connect the math to an experiment you can reproduce and defend.
For Python developers with basic ML and PyTorch familiarity. Dates and live pacing remain provisional; GPU requirements and costs will be confirmed before enrollment opens.
What you’ll learn
Implement RL and LLM post-training, then use held-out evaluations to make defensible production decisions.
Define a task, baseline, data split and reward or preference signal before training.
Implement a runnable pipeline with configuration, dependency and compute records.
Compare baseline and adapted models on held-out examples with error analysis.
Connect MDPs and value learning to policy gradients and actor-critic methods.
Inspect PPO learning curves, reward design, instability and implementation errors.
Use controlled comparisons to distinguish algorithm choices from experimental noise.
Compare preference optimization and verifiable-reward training with simpler baselines.
Identify leakage, reward exploitation, quality regressions and compute tradeoffs.
Defend a release gate, rollback plan and next experiment with evidence.
Learn directly from expert instructors
Dr. Rajat Dandekar
Vizuara Co-founder | Purdue PhD | Teaching engineers to build AI systems
Dr. Raj Dandekar
Vizuara Co-founder | MIT PhD | Scientific machine learning & practical AI
Dr. Sreedath Panat
Vizuara Co-founder | MIT PhD | Computer vision & scientific machine learning
Who this course is for
ML engineers and applied researchers comfortable with Python and basic ML who want to implement RL and LLM post-training.
Software engineers with PyTorch experience who want to understand policy optimization, reward design and rigorous evaluation.
What's included
Lifetime access
Go back to course content and recordings whenever you need to.
Community of peers
Stay accountable and share insights with like-minded professionals.
Certificate of completion
Share your new skills with your employer or on LinkedIn.
RLVR Arena capstone
Train a small model on Countdown puzzles. Implement GRPO advantages and loss, evaluate with an exact arithmetic verifier, and submit a checkpoint, code, reward curve and short experiment report.
Original lecture library and research projects
Seven foundation lectures and six Phase 2 project units, with links to the original slides, mathematical companions, code, assignments and research write-ups.
Maven Guarantee
Your purchase is backed by the Maven Guarantee.
Course syllabus
Week 1
Phase 1 — RL foundations and policy gradients
Week 2
Phase 1 — PPO, RLHF, GRPO and DPO
Your capstone: RLVR Arena — teach a model to solve Countdown
Use the original Vizuara RLVR Arena project to connect policy optimization with a task you can verify exactly.
TASK — Combine every supplied number exactly once to reach a target. Train Qwen2.5-0.5B-Instruct to reason and return a valid arithmetic expression.
IMPLEMENT — Complete group-relative advantages and the clipped, KL-regularized GRPO loss in the provided starter.
EVALUATE — Use public development puzzles and an exact arithmetic verifier. The original assessment reruns submitted checkpoints on a private held-out set.
SUBMIT — Your model checkpoint, training code, predictions, reward curve and a short report with an ablation and failure analysis.
Original brief and code: https://github.com/VizuaraAI/RL-in-Production-Bootcamp-Resources/tree/main/lectures/capstone
Final live pacing, submission date and leaderboard arrangements will be confirmed before enrollment opens.
What learners say about earlier Vizuara programs
“Excellent coursework and teaching style.”
Omnaath Guptha · AV Software Simulation and Test Lead
Review of Vizuara’s Modern Robot Learning Bootcamp.
This feedback describes a previous Vizuara program, not this new Maven RL cohort. Read the original review and more learner experiences at https://reviews.vizuara.ai/
Vizuara: first principles, working code, proven teaching
Vizuara brings first-principles explanations, live coding, research papers and hands-on projects to a global AI learning community. Our YouTube channel has 224,000 subscribers, and our platform has more than 19,000 registered accounts across school, institutional and professional programs.
Founded by Purdue PhD Dr. Rajat Dandekar and MIT PhDs Dr. Raj Dandekar and Dr. Sreedath Panat, all IIT Madras alumni. The founders co-authored Manning’s Build a DeepSeek Model (From Scratch).
Vizuara’s collection of 109 learner stories reports a 4.96/5 average across rated reviews, with reviewers from organizations including Bosch, ISRO and Confluent. This feedback comes from earlier Vizuara programs.
Read learner stories: https://reviews.vizuara.ai/
Watch our teaching: https://www.youtube.com/@vizuara
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