Gotta Learn Fast: A new benchmark for generalization in RL

Rethinking RL Evaluation: Can Benchmarks Truly Reveal Failures of RL Methods?

arXiv:2510. 10541v2 Announce Type: replace-cross Abstract: Current benchmarks are inadequate for evaluating progress in reinforcement learning (RL) for large language models (LLMs).

RL²: Fast reinforcement learning via slow reinforcement learning

Certificate-Guided Evaluation of Reinforcement Learning Generalization

arXiv:2606. 00840v1 Announce Type: new Abstract: This work presents a logic-driven framework to evaluate the performance of reinforcement learning (RL) algorithms in their ability to generalize to unseen tasks.

Geometry-Preserving Orthonormal Initialization for Low-Rank Adaptation in RLVR

arXiv:2606. 31813v1 Announce Type: cross Abstract: Low-rank adaptation (LoRA) and its variants enable parameter-efficient fine-tuning of large language models under the supervised fine-tuning (SFT) paradigm.

The Generalization Spectrum: A Chromatographic Approach to Evaluating Learning Algorithms

arXiv:2606. 25450v1 Announce Type: new Abstract: Traditional evaluations measure a learning algorithm's final performance on an i.

Exposure-Based Reinforcement Learning to Rank

arXiv:2607. 18689v1 Announce Type: new Abstract: Reinforcement learning (RL) methods for learning-to-rank (LTR) can optimize (almost) any ranking goal, e.