Osmosis

Osmosis is a forward-deployed reinforcement learning (RL) post-training platform that helps companies build task-specific models that outperform generic foundation models at a fraction of the cost. Unlike broad fine-tuning services, Osmosis focuses on the post-training side of RL—engineers use it to apply advanced techniques like GRPO and DAPO, and to train multi-turn tool use, without wrestling with infrastructure. The platform covers the full post-training workflow, from feature engineering to reward function creation, and the Osmosis team works hands-on with your training and serving processes. That means Osmosis isn't a self-serve tool; it's a collaborative engagement where the vendor works directly with you to hit performance and adherence targets. The platform's core value is in its RL-specific capabilities. It uses Fused Logprobs to cut memory in long-context RL, which matters for agentic workloads with many tool calls. It can train thousands of LoRA adapters concurrently, treating adapters as cheap policies that share a base model. It supports LoRA MoE RL for Qwen3.5, pairing Megatron-LM with SGLang for training and inference clusters. These features are technical and deep—they signal that Osmosis is built for teams that need heavy customization, not for casual tinkerers. Osmosis also handles the improvement loop. It integrates with your evaluation solutions to monitor performance and automatically start retraining runs when needed, ingesting real-time data and updating models in as little as every hour. This is a key differentiator for production environments where models must stay fresh. Use cases the vendor highlights are data extraction with schema precision, teaching agents to use production tools reliably, and code generation for domain-specific languages. A major selling point is full model ownership. You can serve models on Osmosis or export them to self-host, which keeps you from being locked into a black-box API. That positions Osmosis as a