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News/To Distill or Decide? Understanding the Algorithmic Trade-off in Partially Observable Reinforcement Learning
arxiv
PublishedOctober 23, 2026 at 4:00 AM

To Distill or Decide? Understanding the Algorithmic Trade-off in Partially Observable Reinforcement Learning

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arXiv:2510.03207v2 Announce Type: replace Abstract: Partial observability is a notorious challenge in reinforcement learning (RL), due to the need to learn complex, history-dependent policies. Recent empirical successes have used privileged expert distillation--which leverages availability of latent

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