Using Jev-style decision models for large-scale trajectory labeling to support RSI
The post argues that trajectory labeling must assess task difficulty, solution correctness, and design diversity, and that Jev-style decision models can greatly cut the token cost of labeling millions of agent trajectories.
This is a very clever usecase for Jev-like decision models. Trajectory labelling at scale is key for RSI. Why is that? Every trajectory needs to be assessed across several dimensions. 1. How difficult was the task? 2. Was the agent’s solution correct? 3. Did the agent consider a diverse set of design choices before deciding to pick a specific route? 4. Is the agent’s thinking directionally correct? Imagine answering these questions for millions of agents each of which spawns tens or hundreds of subagents! Token cost for trajectory labelling needs to be driven down a lot to do this at scale. This work is unique and solves a rather under-looked aspect of RL research.
This post quotes another post: x.com/teknopawn/status/2108280128841318669