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When we put lots of text (eg a code repo) into LLM context, cost soars b/c of the KV cache’s size. What if we trained a smaller KV cache for our documents offline? Using a test-time training recipe we call self-study, we find that this can reduce cache memory on avg 39x

🚨New preprint 🚨 Turning Down the Heat: A Critical Analysis of Min-p Sampling in Language Models We examine min-p sampling (ICLR 2025 oral) & find significant problems in all 4 lines of evidence: human eval, NLP evals, LLM-as-judge evals, community adoption claims 1/8

Excited to share 🤯 that our LMUnit models with Contextual AI just claimed the top spots on RewardBench2 🥇 How did we manage to rank +5% higher than models like Gemini, Claude 4, and GPT4.1? More in the details below: 🧵 1/11

Shrinking the Generation-Verification Gap with Weak Verifiers "we introduce Weaver, a framework for designing a strong verifier by combining multiple weak, imperfect verifiers." "Weaver leverages weak supervision to estimate each verifier’s accuracy and combines their outputs

LLMs can generate 100 answers, but which one is right? Check out our latest work closing the generation-verification gap by aggregating weak verifiers and distilling them into a compact 400M model. If this direction is exciting to you, we’d love to connect.

What a throwback to weak supervision! Great work Jon Saad-Falcon Kelly Buchanan Mayee Chen!

Very exciting work on using weak supervision for RL- closing the “generation-verification gap”!! Once again- principled approaches to labeling/data development are the keys!

LLMs often generate correct answers but struggle to select them. Weaver tackles this by combining many weak verifiers (reward models, LM judges) into a stronger signal using statistical tools from Weak Supervision—matching o3-mini-level accuracy with much cheaper models! 📊

Verdict systems can now judge image inputs. Score product photos. Ad creatives. UI mockups. Haize anime birds. Judge any thing for any quality—and understand why.

Introducing Weaver, a test time scaling method for verification! Weaver shrinks the generation-verification gap through a low-overhead weak-to-strong optimization of a mixture of verifiers (e.g., LM judges and reward models). The Weavered mixture can be distilled into a tiny

See Jon Saad-Falcon's post for more details: x.com/JonSaadFalcon/… Paper: arxiv.org/abs/2506.18203 Blog: hazyresearch.stanford.edu/blog/2025-06-1… github.com/HazyResearch/s…… Datasets and Models: huggingface.co/collections/ha…