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  "text": "// Self-play with a pinch of human data //\n\nReally cool paper combining human demonstrations and self-play RL.\n\n30 minutes of human data, 2500x less than imitation learning, is enough to make self-play policies coordinate with real people.\n\nPure self-play learns effective but alien conventions that humans cannot drive alongside. The usual fix is brittle reward engineering and domain randomization.\n\nThis work instead treats a small set of human demonstrations as a regularization objective on top of a minimal safe goal-reaching reward.\n\nWhy does it matter?\n\nThe resulting policies coordinate with held-out human trajectories and finish training in 15 hours on a single consumer GPU. The lesson travels well past driving. A small demonstration regularizer may be the cheapest alignment knob we have for self-play.\n\nPaper: https://t.co/nLrVwRFEW9\n\nLearn to build effective AI agents in our academy: https://t.co/LRnpZN7L4c",
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