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Posted: 2025-06-06 10:21:19 UTC
This article contains some claims that remain unverified. While much of the content may be accurate, exercise care when relying on this information.
This article contains some claims that remain unverified. While much of the content may be accurate, exercise care when relying on this information.
Status
Last Updated
2025-06-06 10:22:10 UTC
Verified By
Rollup News
Scaling up data and compute is not enough for RL to solve complex tasks due to the horizon. Horizon reduction techniques, like SHARSA, substantially improve scalability by addressing bias accumulation in TD learning.
Scaling RL with data and compute alone is insufficient for complex tasks.
Horizon reduction is crucial for improving the scalability of offline RL.
Bias accumulation in TD learning is a significant obstacle to RL scalability.
The SHARSA method, based on BC and SARSA, enhances scalability by reducing the horizon.
Poor scaling behavior of offline RL algorithms despite increased data.
Bias accumulation in TD learning over long horizons.
Difficulty in solving complex tasks with standard RL methods.