We have made important strides towards solving two manual bottlenecks to continual learning: reward, and envrionments.
Continual grading: Rather than needing to manually label thousands of traces, we present a metagrader - a model that can learn off of a subset of representative reward.
Group Size 1 (GS1): Instead of methods like GRPO that require groups and therefore offline environments, we explore methods were you only rollout once, proposing a stabilized algorithm GS1, where the environment becomes the product itself.
We test both of these methods on frontier, economically valuable benchmarks like PRBench, Harvey LAB, and AutomationBench and achieve frontier results.
View full blog posts below, these two pieces of our research are in private beta on the Trajectory platform
Continual grading: Rather than needing to manually label thousands of traces, we present a metagrader - a model that can learn off of a subset of representative reward.
Group Size 1 (GS1): Instead of methods like GRPO that require groups and therefore offline environments, we explore methods were you only rollout once, proposing a stabilized algorithm GS1, where the environment becomes the product itself.
We test both of these methods on frontier, economically valuable benchmarks like PRBench, Harvey LAB, and AutomationBench and achieve frontier results.
View full blog posts below, these two pieces of our research are in private beta on the Trajectory platform
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