Osmosis

Osmosis is a comprehensive post-training platform that enables companies to apply reinforcement learning (RL) techniques to fine-tune AI models for specific tasks. It supports cutting-edge RL algorithms like GRPO and DAPO, and allows engineers to train models without managing infrastructure. The platform is designed for teams that need to build reliable, multi-step AI agents—whether for data extraction, tool use, or code generation. Who it's for: AI teams at companies that require task-specific models that outperform generic foundation models on their own data and workflows. It is especially useful for organizations with complex, multi-turn agentic use cases where prompt engineering falls short. How it works: Osmosis provides a hands-on deployment service, working directly with customers on the entire post-training workflow—from feature engineering to reward function creation. It also offers continuous improvement by integrating with evaluation solutions to automatically retrain models as new data comes in, with updates possible as frequently as every hour. Recent innovations include fused logprobs for reduced memory in long-context RL and concurrent training of thousands of LoRA adapters. What makes it different: Osmosis is the first reinforcement fine-tuning platform focused on practical real-world agent challenges. It offers full model ownership—users can serve models on Osmosis or export them to self-host. The platform also supports multi-turn tool training and leverages advanced RL techniques like LoRA-based training for large models.