This document presents research on improving power grid management through reinforcement learning. The authors introduce a model-free approach using a masked action space that allows agents to learn optimal control strategies without requiring predefined expert knowledge. Different grid state representations are explored, including novel graph-based methods, and evaluated on their effectiveness in reducing power losses and enhancing grid stability. Experimental results using a simulated environment demonstrate the superior performance of their approach, particularly when incorporating graph observations and training against an opponent to improve robustness.

This research presents LGTC-IPPO, a novel decentralized reinforcement learning approach designed for allocating diverse resources among multiple agents. The core innovation lies in its integration of dynamic cluster agreements, allowing agents to form temporary teams based on resource needs without relying on centralized control. Evaluated against other multi-agent reinforcement learning methods and even a centralized expert, the proposed system demonstrates enhanced performance, coordination, and stability in dynamic environments, particularly as the number of agents and resource types increases. The effectiveness of this decentralized strategy, especially in facilitating efficient resource reallocation when conditions change, highlights its potential for real-world applications where global coordination is challenging. The study also validates the approach through both extensive simulations and hardware experiments using drones.

This document details a reinforcement learning approach for enabling humanoid robots with multi-fingered hands to perform dexterous manipulation tasks based on visual input. The core challenges addressed include bridging the simulation-to-real-world gap, designing effective reward functions, improving policy learning sample efficiency, and handling object perception. The research introduces several novel techniques such as an automated real-to-sim tuning module, a generalizable reward scheme using contact and object goals, strategies for more sample-efficient policy learning through initialization and distillation, and a combined approach to object representation using both sparse and dense features. These methods demonstrate promising results on real-world tasks like grasping, lifting boxes, and bimanual handover, highlighting the potential for robust generalization and high performance without requiring human demonstrations.

This document introduces µCODE, a novel approach for generating code iteratively based on execution feedback, departing from complex multi-turn reinforcement learning by leveraging the insight that code generation is a one-step recoverable process. µCODE employs a simple yet effective strategy, iteratively training a generator to propose code solutions and a verifier to evaluate them based on single-step rewards. This method not only demonstrates significant improvements over existing state-of-the-art techniques on code generation benchmarks but also offers a more stable and efficient training process. The paper provides theoretical analysis supporting the effectiveness of µCODE and explores the benefits of using a learned verifier for both training and optimizing inference through a multi-turn Best-of-N search strategy.

This pod introduces Confidence-Reward driven Preference Optimization (CRPO), a novel method for improving machine translation by more effectively selecting training data for large language models (LLMs). The paper highlights challenges in applying LLMs to translation due to pretraining on English-centric data and the complexity of traditional reinforcement learning from human feedback. While Direct Preference Optimization (DPO) simplifies training, its success relies on high-quality preference data. CRPO addresses this by combining reward scores with model confidence to identify challenging sentence pairs where the model is uncertain or underperforming, leading to more efficient fine-tuning. The authors demonstrate CRPO's effectiveness on both LLMs and encoder-decoder models, showing it outperforms existing data selection methods in translation accuracy and data efficiency.

This academic paper introduces MiCRo, a two-stage framework designed to improve how Large Language Models (LLMs) learn and adapt to diverse human preferences, moving beyond the traditional assumption of a single universal preference.  Reward modeling, a key component in aligning LLMs with human feedback, typically uses a single model that struggles with varied preferences. The authors demonstrate theoretically that relying on a single model for diverse preferences leads to unavoidable errors. MiCRo addresses this by first using a mixture model to identify different preference subgroups from standard binary data, then employing a context-aware routing strategy to personalize responses dynamically based on limited user information. Experimental results indicate that MiCRo effectively captures diverse preferences and enhances personalized performance compared to single models and other baselines.

This document introduces Prolonged Reinforcement Learning (ProRL), a new training method designed to significantly enhance the reasoning abilities of large language models. By implementing KL divergence control and reference policy resetting, ProRL maintains training stability over extended periods, allowing models to discover novel reasoning strategies and outperform base models across a variety of tasks including math, code, STEM, and logic puzzles. The research indicates that RL is particularly effective for tasks where the base model initially struggles, and that these sustained training gains demonstrate a genuine expansion of reasoning boundaries, even on unseen tasks. The work highlights the potential of long-horizon RL to create more capable and generalizable AI systems, exemplified by their Nemotron-Research-Reasoning-Qwen-1.5B model.

This academic paper introduces PROXYTHINKER, a novel inference-time method designed to enhance the visual reasoning abilities of large vision-language models (LVLMs). Unlike computationally expensive fine-tuning approaches like reinforcement fine-tuning (RFT), PROXYTHINKER allows larger models to inherit reasoning skills from smaller, pre-trained reasoning models. It achieves this by adjusting the large model's output based on the difference between a small RFT expert's output and a small base model's output. The paper demonstrates that this training-free techniquesignificantly improves performance on various visual reasoning benchmarks while being computationally efficient.

This academic paper presents Open CaptchaWorld, a novel benchmark dataset designed to assess the ability of multimodal AI agents to solve complex, multi-step CAPTCHAs encountered in real-world online environments. Unlike existing benchmarks that focus on static, single-turn tasks, Open CaptchaWorld emphasizes the interactive and dynamic nature of modern human verification puzzles. Through empirical analysis using the benchmark, the research demonstrates that while state-of-the-art multimodal models can handle basic visual tasks, they significantly lag behind human performance on challenges requiring more complex reasoning, fine-grained operations, or strategic understanding. The study highlights the current limitations of AI agents in tackling CAPTCHAs and provides insights for future development in this area.

This document presents DexMachina, a novel curriculum-based reinforcement learning algorithm for functional retargeting in bimanual dexterous manipulation. The method focuses on teaching robot hands to replicate human object manipulation trajectories from demonstrations, particularly for articulated objects and complex, long-horizon tasks. By employing virtual object controllers that gradually reduce their influence and providing auxiliary rewardsbased on motion and contact, DexMachina allows the robot policy to learn and adapt to the unique capabilities of different dexterous hands. The paper also introduces a simulation benchmark for evaluating both functional retargeting algorithms and various dexterous hand designs, demonstrating DexMachina's superior performance compared to baseline methods across a range of hands and tasks.