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Goal-conditioned RL (GCRL) is great - unsupervised, can use data (in offline mode), flexibility to define tasks at test time. But can we run GCRL on language data?? In our new work we show that language GCRL enables sophisticated test-time reasoning for interactive tasks! 🧵👇

Sergey Levine

119,247 subscribers

18,782 views • 1 year ago •via X (Twitter)

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Sergey Levine1 year ago

The idea is to train goal-conditioned value functions on interaction data (the game Avalon, WebShop, etc.). These value functions predict how likely different outcomes are given a high-level action, e.g., if I accuse Player 2 in Avalon, how likely is that to raise suspicion?

Sergey Levine1 year ago

At evaluation time, the agent can use these goal-conditioned value functions as part of a test-time compute self-refinement loop, evaluating its actions along many different dimensions via long-horizon prediction.

Sergey Levine1 year ago

Here are some examples of real interactions with our agent, including reasoning and prediction from the value functions.

Sergey Levine1 year ago

A really fun project led by Joey Hong, with @ancadianadragan Website: Paper:

Wendy Carlosa1 year ago

gcrl doesn't iterate long enough to see the immorality of making new children when I hate the ones you already did

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