Fine-Tuning Diffusion Language Models with Context Selection and Target Weighting
Supervised fine-tuning of discrete diffusion language models masks some response tokens and trains the model to recover their original values from the visible context. The masking pattern therefore determines both the context available to the model and the tokens it learns to…
- 01.Supervised fine-tuning of discrete diffusion language models masks some response tokens and trains the model to recover their original values from the visible context
- 02.The masking pattern therefore determines both the context available to the model and the tokens it learns to…
This material is provided as an extended analytical summary.
Read full article at sourceRelated Intelligence
All stories →It's happening...
One of the most controversial companies in history just filed to go public. And yes, we're talking about Anthropic. The same company that says there's a greater than 10% chance AI kills all of humanity is also simultaneously one of the best built businesses of all time. And in…
OpenAI announces three big science discoveries and a new way to work with academics
Less than a month after its mass release of hundreds of manuscripts that it said contained solutions to long-standing mathematics questions, OpenAI is back with three more "major" discoveries in math, computer science, and physics. What's different this time is the choice to…
How Sullivan & Cromwell builds legal tools with Codex | OpenAI
At Sullivan & Cromwell LLP, John Savva uses Codex to build tools around the firm’s legal expertise. Agreement Analyzer brings together a draft agreement, selected precedents, and playbooks reflecting the firm’s and client’s positions. It identifies potential issues and returns a…