MENGENAI EPISOD INI
Today’s agents are stuck in what Ronak Malde calls “pseudo continual learning”: intelligent, but updated in offline batches, like a paper road map you can’t reroute mid-trip. Ronak, co-founder of Trajectory, explains why closing that loop is the next learning paradigm, and why training your own model, which used to take a dedicated research team, months of work and hundreds of millions in compute, is now something a company can do in a few clicks with their own data. He details two research bets they are making available to everyone with their product: 1) a variant of GRPO that removes the need for a simulated environment by learning from single real-world rollouts instead of 16 replays of the same task, and 1) a meta-grader that increased data efficiency by 100x. Ronak explains how Harvey trained a model six times cheaper than frontier, improving an open-source baseline by over 4x on Harvey's own legal benchmark. He defines the difference between continual learning and recursive self-improvement and predicts product builders will become gardeners, setting trellises for products that grow themselves.
Hosted by Pat Grady and Lauren Reeder, Sequoia Capital
0:00 – Introduction
0:45 – Pseudo continual learning: paper maps vs Google Maps
2:48 – Do we need new architectures? Rich Sutton, David Silver, and the missing layer
4:33 – From a $100M consulting model to a product: lessons from SWE-1 at Windsurf
7:36 – Harvey: 6x cheaper than frontier, 4% to 18% on its own benchmark
8:55 – Why "Trajectory": the unit of work is a task, not a token
10:13 – The primitives of continual learning: traces, replayable environments, reward
12:56 – Group Size 1 and the meta-grader: training without an environment or a labeled reward
16:16 – The platform: train, eval and deploy in one click, and who it's for
18:58 – Rogo and intelligence density
20:14 – What's next: curiosity, and why continual learning beats RSI on alignment
24:19 – Research and product under one roof, and the product builder as gardener
27:26 – Why work at Trajectory, and the optimistic case: from 1% to 99%
