Research to Advance AI
Scale Labs advances AI through research. Our research focuses on agents, post-training, reasoning, safety, evaluation, and alignment, and the science of data.
[LEADERBOARDS]
Benchmarks for frontier, agentic, and safety capabilities
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Model-preference rankings from real-world usage.
[PAPERS]
Research papers and publications covering agents, post-training, reasoning, safety, evaluation, and alignment, and the science of data.
Not Every Rubric Teaches Equally: Policy-Aware Rubric Rewards for RLVR
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Insights, analysis, and updates from Scale Labs
The Path to Large Scale Dense Video Captioning
We ran dozens of experiments on dense captioning for robot manipulation video. The biggest lever turned out to be how we represented the video to the model. Most techniques from the literature added noise on smaller models.
57 Healthcare Professionals Told Us What They Need from AI
We surveyed 57 healthcare professionals about what they actually want from AI. Their answers point to three capability gaps that current evaluations miss.
Coverage Not Averages: Rethinking Retrieval Evaluation
A single benchmark score suggests stability and completeness. In reality, it may reflect performance on a narrow and biased slice of the problem.
Improving Multi-Turn Tool Use with GRPO: Results and Insights
We’re sharing early insights from applying GRPO reinforcement learning to multi-turn tool-use tasks using our MCP Tool Use dataset. In a controlled experiment with 3,000 samples, we fine-tuned Qwen2.5-14B using LoRA (rank 32) and evaluated it on MCP Atlas. We observed significant improvement in both coverage rate and pass rate. In this article, we share observations on how data quality, reward design, and training constraints interact in agentic training settings.
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