the beef needed infrastructure. so i put @tunahorse21 in x fighter. he deploys agents and throws people into a j*b
Tuna puts a grappler’s threat behind projectiles: respect the fireball and give him room to approach, or challenge him and risk the grab.
His projectiles have real combo routes, too. We proved a two-hit medium-punch → heavy Deploy link and a four-hit light-punch → crouching light-punch → EX Deploy sequence.
The highlight is SA3: Osoto Kuzushi Sweep. Grip fight, collar tie, calf contact, opponent flip, heavy landing.
that’s a fast response (to Jev)
Introducing Contrastive Language Model (CLM): an ultra-fast System One Model trained with a contrastive learning objective that connects states and actions.
CLM-8B is pre-trained on internet-scale data and delivers up to 9× faster inference than Jev ⚡ while achieving comparable performance across computer-use, gaming, and tool-calling tasks.
With lightweight fine-tuning, CLM-8B sets a new SOTA on challenging agentic coding benchmarks, such as DeepSWE (81.6%) and Terminal-Bench 2.1 (87.6%). In contrast, Jev fails to serve as an effective verifier for these long-horizon tasks.
We also build an efficient training and serving infra for CLMs by disaggregating states and actions, allowing their embeddings to be cached and reused independently. This substantially reduces inference latency in settings where the state evolves continuously while the action set remains fixed.
Finally, we establish scaling laws for CLMs and show that the test contrastive loss decreases predictably as a power law in training compute, model size, and dataset size.
📄 Blog: contrastive-lm.notion.site
💻 Code: github.com/Contrastive-LM/CL…
🗣️ Discord: discord.gg/5dAQEDJBs
🤗 Data & Models: huggingface.co/Contrastive-L…
More details on CLM’s architecture, data recipe, and scaling laws in the thread below 🧵
















