ARC-AGI-3 rewards learning from fewest actions, challenging brute-force RL
Ryan__Rhys · x · 2026-11-06
After playing the ARC-AGI-3 games, RyanRhys notes they recall the Atari RL benchmarks from over a decade ago, with a crucial difference: ARC-AGI-3 explicitly rewards agents for learning from as few actions as possible, demanding extreme sample efficiency. Classical RL approaches that brute-force environments with many rollouts may not fit this new benchmark.
Related event: ARC-AGI-3 Games Open for Public Playtesting(3 posts)→
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