kaal:claim:4855607-014

Deep reinforcement learning demands large amounts of training data, which suggests its algorithms differ fundamentally from human learning, and learning without supervision becomes particularly hard when rewards are sparse, as they typically are in sequence generation tasks.

Deep RL methods often demand large amounts of training data, suggesting that the algorithms may differ fundamentally from those underlying human learning. Moreover, learning without supervision is particularly hard when the reward is sparse, which is likely to happen for sequence generation tasks.

Wulf A. Kaal, How AI Models are Optimized Through Web3 Governance (2024), Model Overview: Reinforcement Learning (RL), p. 26
https://ssrn.com/abstract=4855607 · source PDF

Wulf A. Kaal, How AI Models are Optimized Through Web3 Governance (2024). SSRN: https://ssrn.com/abstract=4855607

• sparse reward settings
• sequence generation tasks

failuresupport: evidencedfailure: Sparse reward learning failurefamily: ai-model-and-training-failureeconomicsempirical-evidence

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