Tootfinder

Computationally efficient Gauss-Newton reinforcement learning for model predictive control
Dean Brandner, Sebastien Gros, Sergio Lucia
https://arxiv.org/abs/2508.02441 https://

Computationally efficient Gauss-Newton reinforcement learning for model predictive control
Model predictive control (MPC) is widely used in process control due to its interpretability and ability to handle constraints. As a parametric policy in reinforcement learning (RL), MPC offers strong initial performance and low data requirements compared to black-box policies like neural networks. However, most RL methods rely on first-order updates, which scale well to large parameter spaces but converge at most linearly, making them inefficient when each policy update requires solving an opt…

This https://arxiv.org/abs/2505.24298 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

AReaL: A Large-Scale Asynchronous Reinforcement Learning System for Language Reasoning
Reinforcement learning (RL) has become a trending paradigm for training large language models (LLMs), particularly for reasoning tasks. Effective RL for LLMs requires massive parallelization and poses an urgent need for efficient training systems. Most existing large-scale RL systems for LLMs are synchronous by alternating generation and training in a batch setting, where the rollouts in each training batch are generated by the same (or latest) model. This stabilizes RL training but suffers fro…

This https://arxiv.org/abs/2505.24718 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCV_…

Reinforcing Video Reasoning with Focused Thinking
Recent advancements in reinforcement learning, particularly through Group Relative Policy Optimization (GRPO), have significantly improved multimodal large language models for complex reasoning tasks. However, two critical limitations persist: 1) they often produce unfocused, verbose reasoning chains that obscure salient spatiotemporal cues and 2) binary rewarding fails to account for partially correct answers, resulting in high reward variance and inefficient learning. In this paper, we propos…

Yep, seems like different parts of the Fosstodon mod team just keep doubling down on "we are apolitical" – as if that was a real thing and not just reinforcing the existing power and abuse…
And in true fashion for FOSS priorities, the admin can't seem to get a statement out as it's more important to first pick a blogging platform.
This could fit into a FOSS-centric reboot of that Silicon Valley comedy show if it wasn't so sad 😅
#fosstodon

AffectGPT-R1: Leveraging Reinforcement Learning for Open-Vocabulary Emotion Recognition
Zheng Lian
https://arxiv.org/abs/2508.01318 https://arxiv.org/pdf/2…

AffectGPT-R1: Leveraging Reinforcement Learning for Open-Vocabulary Emotion Recognition
Open-Vocabulary Multimodal Emotion Recognition (OV-MER) aims to predict emotions without being constrained by predefined label spaces, enabling fine-grained and human-like emotion understanding. Unlike traditional discriminative methods, OV-MER leverages generative models, such as large language models (LLMs) with extensive vocabularies, to capture the full spectrum of emotions. Previous approaches (like AffectGPT) primarily rely on token-level loss for training. However, this objective does no…

This https://arxiv.org/abs/2501.09622 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qu…

Optimizing hypergraph product codes with random walks, simulated annealing and reinforcement learning
Hypergraph products are quantum low-density parity-check (LDPC) codes constructed from two classical LDPC codes. Although their dimension and distance depend only on the parameters of the underlying classical codes, optimizing their performance against various noise channels remains challenging. This difficulty partly stems from the complexity of decoding in the quantum setting. The standard, ad hoc approach typically involves selecting classical LDPC codes with large girth. In this work, we fo…

This https://arxiv.org/abs/2505.10033 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

Evaluating Robustness of Deep Reinforcement Learning for Autonomous Surface Vehicle Control in Field Tests
Despite significant advancements in Deep Reinforcement Learning (DRL) for Autonomous Surface Vehicles (ASVs), their robustness in real-world conditions, particularly under external disturbances, remains insufficiently explored. In this paper, we evaluate the resilience of a DRL-based agent designed to capture floating waste under various perturbations. We train the agent using domain randomization and evaluate its performance in real-world field tests, assessing its ability to handle unexpected…

Brazil: Protect the mangroves and tropical forests of Tauš-Mirim! https://www.rainforest-rescue.org/petitions/1305/brazil-protect-the-mangroves-and-tropical-forests-of-taua-mirim

#petition Brazil: Protect the mangroves and tropical forests of Tauá-Mirim!
The people of the Tauá-Mirim region in the Amazon are urgently calling on the Brazilian government to protect their land. The rivers, mangroves and tropical forests south of São Luís in the state of Maranhão are threatened by infrastructure, industrial, and agricultural projects. Please support our petition.

polyBART: A Chemical Linguist for Polymer Property Prediction and Generative Design
Anagha Savit, Harikrishna Sahu, Shivank Shukla, Wei Xiong, Rampi Ramprasad
https://arxiv.org/abs/2506.04233

polyBART: A Chemical Linguist for Polymer Property Prediction and Generative Design
Designing polymers for targeted applications and accurately predicting their properties is a key challenge in materials science owing to the vast and complex polymer chemical space. While molecular language models have proven effective in solving analogous problems for molecular discovery, similar advancements for polymers are limited. To address this gap, we propose polyBART, a language model-driven polymer discovery capability that enables rapid and accurate exploration of the polymer design …

Multi-Agent Reinforcement Learning for Dynamic Pricing in Supply Chains: Benchmarking Strategic Agent Behaviours under Realistically Simulated Market Conditions
Thomas Hazenberg, Yao Ma, Seyed Sahand Mohammadi Ziabari, Marijn van Rijswijk
https://arxiv.org/abs/2507.02698

Multi-Agent Reinforcement Learning for Dynamic Pricing in Supply Chains: Benchmarking Strategic Agent Behaviours under Realistically Simulated Market Conditions
This study investigates how Multi-Agent Reinforcement Learning (MARL) can improve dynamic pricing strategies in supply chains, particularly in contexts where traditional ERP systems rely on static, rule-based approaches that overlook strategic interactions among market actors. While recent research has applied reinforcement learning to pricing, most implementations remain single-agent and fail to model the interdependent nature of real-world supply chains. This study addresses that gap by evalu…