Reinforcement learning (RL) has become the most widespread and common post-training method for frontier and open models. However, most published training methods or approaches are validated on only a subset of math and coding benchmarks, or not disclosed/shared at all. Teams such as ours that put RL into production find there is rarely a free lunch: environments test niche behaviors, training and inference mismatch, and methods proclaim generalization across domains but deliver none.

At Baseten, we run post-training across real deployments in coding, legal, financial, healthcare, productivity, and voice. Through this work, we have identified common principles for applying RL and related methods to real-world tasks. This workshop shares these lessons, including:

1. Verifying the correctness of a training stack before changing learning algorithm.
2. Auditing RL environments for reward hacking.
3. Using pass@k and better group statistics to a priori predict how RL runs will work/learn.
4. Understanding the interplay between SFT/distillation and RL.

We will also cover popular techniques we have tested and found fickle or unable to generalize.

We will then provide a live, end-to-end demonstration of Loops, our Tinker-compatible training SDK for async RL on dense and MoE open models. Here we will:

1. Define an environment and reward.
2. Launch an RL run.
3. Inspect rollouts and groups live as the run progresses.
4. Catch a reward hack in the traces.
5. Deploy the resulting checkpoint to an inference endpoint.

We aim to have attendees leave with a practical checklist for real-world RL and open environments and code to reproduce the demo we give.

Our proposed format (180 min):

0–20 min: Why production RL is different.
20–90 min: Principles that work, and ones that don't, with evidence from public Base Labs studies.
90-115 min: Break
115–150 min: Live demo, from rollouts and a training run through to deployment.
150–180 min: Q&A.