content type paperpublished October 2026
RLTL;DR: Self-Improvement by Internalizing Self-Generated Feedback
AuthorsMichael Kirchhof, Eleonora Gualdoni, Andrew Szot, Khashayar Gatmiry, Aryo Lotfi, Abbas Kazerouni, Omar Attia, Sanjoy Chowdhury, Alexander Toshev
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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. This becomes problematic in the realms of self-improvement, where tasks are so difficult that the agent has a low or even no chance of success, and where there are no teacher models or example solutions to distill from. In this paper, we introduce RLTL;DR. After each failed attempt, we show the policy the verifier outputs and let it write its own feedback, in the form of a single TL;DR insight. The next rollout is conditioned on all previous insights, and we sequentially sample rollouts until a solution is found. Moreover, we enable backpropagation on the in-context insights to internalize a direct task → insight mapping. On challenging tool-calling and coding datasets (filtered to Pass@128 = 0), standard GRPO training of a Qwen 3.5 9B Thinking policy stays flat at a Pass@1 of 0% to 1%. RLTL;DR breaks through this learning barrier, achieving a Pass@1 of 14–31% with insights in context during training and, crucially, 12–13% when no insight is in context at eval time. We identify that the key is the task → insight internalization. To study this further, we reduce our approach to SFTL;DR, training only on (task, insight) tuples, without showing or backpropagating on any rollouts. Training on only 4k of these tuples recovers almost the full performance of RLTL;DR and classical SFT on full rollouts. This demonstrates a promising compacted training paradigm of the form “on this sort of task, keep this sort of thing in mind”, which we hope to inspire future research on.
DR-MPC: Deep Residual Model Predictive Control for Real-World Social Navigation
March 6, 2025research area Computer Vision, research area Methods and Algorithmsconference IEEE Robotics and Automation Letters
How can a robot safely navigate around people with complex motion patterns? Deep Reinforcement Learning (DRL) in simulation holds some promise, but much prior work relies on simulators that fail to capture the nuances of real human motion. 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. By blending MPC with model-free DRL, DR-MPC overcomes…
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Apple Workshop on Machine Learning for Health 2023
November 8, 2023research area Health
Earlier this year, Apple hosted the Workshop on Machine Learning for Health. This two-day hybrid event brought together Apple and the academic research community and clinicians to discuss state-of-the-art machine learning (ML) research in health.
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