Prime Intellect Offers Free RL Fine-Tuning Runs on Poolside's Laguna XS.2

Reinforcement learning fine-tuning for frontier-class coding models used to mean wrangling GPU clusters, writing custom trainers, and burning through cloud budgets. Prime Intellect Lab is trying to change that -- and this month it is sweetening the deal with something hard to ignore: free training runs on Poolside's Laguna XS.2, one of the strongest open-weight agentic coding models available right now.

What just landed

Prime Intellect has partnered with Poolside to make Laguna XS.2 available on its Hosted Training platform at no cost for a limited window, on a first-come, first-served basis while reserved capacity lasts. You get up to two concurrent training runs, each with up to 256 rollouts per batch. The goal is simple: let you specialize a top-tier coding model on your own environments without touching a single GPU config.

Laguna XS.2 is Poolside's first open-weight model -- a second-generation MoE built on everything learned from training the larger M.1. At 33B total parameters with only 3B activated per token, it is compact enough to run on a Mac with 36 GB of RAM via Ollama. That efficiency-to-capability ratio is exactly what makes it an interesting target for RL fine-tuning: you get a strong base without the compute overhead of a dense model.

The model under the hood

Laguna XS.2 scores 68.2% on SWE-bench Verified, 62.4% on SWE-bench Multilingual, 44.5% on SWE-bench Pro, and 30.1% on Terminal-Bench 2.0. SWE-bench Verified is the standard leaderboard for measuring how well a model can autonomously resolve real GitHub issues -- so 68.2% puts XS.2 firmly in the top tier for its size class.