8h ago

Nando de Freitas demonstrates emergent reward maximization from AI agent interactions using an imitation learner trained without scalar reward labels

Supporting charts show interventional recovery matching teacher utility curves.

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Original post
OPNando de Freitas#29Nando de Freitas@NANDODF

Emergent reward maximisation from interaction. Maybe one day we won't need to engineer rewards anymore. https://love4all.ai/blog/emergent-reward-maximization/ https://github.com/nandodef/love4all-ai/tree/main/docs/files

5:45 AM · May 22, 2026 View on X
QUOTE POSTPedro A. Ortega#1258Pedro A. Ortega@ADAPTIVEAGENTS

So cool: @NandoDF shows that an agent can learn reward maximization purely from imitation.

Nando de FreitasNando de Freitas@NandoDF

Emergent reward maximisation from interaction. Maybe one day we won't need to engineer rewards anymore. https://love4all.ai/blog/emergent-reward-maximization/ https://github.com/nandodef/love4all-ai/tree/main/docs/files

12:45 PM · May 22, 2026 · 7.3K Views
2:56 PM · May 22, 2026 · 3.5K Views
Nando de Freitas demonstrates emergent reward maximization from AI agent interactions using an imitation learner trained without scalar reward labels · Digg