用 Jev 类决策模型做大规模轨迹标注以支撑 RSI
评论指出轨迹标注需评估任务难度、解法正确性、设计多样性等维度，Jev 类决策模型能把百万级 agent 轨迹的标注 token 成本大幅压低。
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.
这条是引用着另一条写的：x.com/teknopawn/status/2108280128841318669