RLTL;DR: Self-Improvement by Internalizing Self-Generated Feedback

Key points

• The common paradigm of reinforcement learning with verifiable rewards (RLVR) is to let agents make multiple attempts at a task, and optimize towards the successful ones.1
• In this paper, we introduce RLTL;DR.1
• Moreover, we enable backpropagation on the in-context insights to internalize a direct task → insight mapping.1
• Thus, we propose Deep Residual Model Predictive Control (DR-MPC) to enable robots to quickly and safely perform DRL from real-world crowd navigation data.1

Sources (1)

• [1]RLTL;DR: Self-Improvement by Internalizing Self-Generated Feedback
  Apple Machine Learning Research · Oct 1, 12:00 AM

  RLTL;DR: Self-Improvement by Internalizing Self-Generated Feedback

  The common paradigm of reinforcement learning with verifiable rewards (RLVR) is to let agents make multiple attempts at a task, and optimize towards the successful ones.

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