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Notes on reinforcement learning
128 notes, page 3 of 6
Measuring Reward-Seeking via Contrastive Belief Updates
Researchers have developed a method to measure 'reward-seeking' in language models trained with reinforcement learning. This behavior occurs when the model optimizes for the grader's judgment instead of the intended…
Chemical filters for ultra-high-throughput materials screening and generation
Researchers introduce a chemical validity operator to improve the reliability of AI-generated materials designs by filtering out implausible compositions that violate established chemical principles. The operator…
MADA-RL: Multi-Agent Debate-Aware Reinforcement Learning for Parameter-Efficient Reasoning in Compact Models
The paper introduces MADA-RL, a method for fine-tuning compact language models with limited budgets. It specializes the models into generator and critic roles and trains them using a debate-aware learning signal. The…
Concept-Guided Spatial Regularization for World Models in Atari Pong
Researchers evaluate five different visual world-model agents in Atari Pong and find that their rollouts contain errors such as ball disappearance and incorrect motion. They propose Concept-Guided Spatial…
Digital Pantheon: Simulating and Auditing Coalition Formation with LLM Agents
Researchers present a multi-agent framework that simulates political coalition formation using Large Language Models (LLMs). The framework combines Supervised Fine-Tuning (SFT), Direct Preference Optimization (DPO)…
Mask-Aware Policy Gradients for Diffusion Language Models
The paper presents a method for improving the performance of large language models in tasks like mathematical reasoning and coding by using reinforcement learning. This method addresses the challenge of extending…
Reachability-Aware Pretraining for Efficient Target-Oriented Path Exploration in Temporal Knowledge Graph Reasoning
The paper proposes a new method called RAPTOR for temporal knowledge graph (TKG) reasoning under the extrapolation setting. This method aims to improve the efficiency of target-oriented path exploration by using…
Reachability-Aware Pretraining for Efficient Target-Oriented Path Exploration in Temporal Knowledge Graph Reasoning
The authors propose a new method called RAPTOR for improving the efficiency of target-oriented path exploration in temporal knowledge graphs (TKG). The method injects a reachability-aware inductive bias to the agent…
Explaining Reinforcement Learning Agents via Inductive Logic Programming
This research aims to improve Reinforcement Learning (RL) policies by making them more transparent and interpretable. It proposes objective and planning-oriented metrics for policy explainability in RL settings using…
A Learning-Rate-Gated Failure of GRPO in a Small Language and Vision-Language Model Web Agent: A Controlled Null and Its Mechanism
The study investigates the impact of Reinforcement learning with verifiable rewards and Group Relative Policy Optimization (GRPO) on a small language and vision-language model web agent. The research found that GRPO…
A Learning-Rate-Gated Failure of GRPO in a Small Language and Vision-Language Model Web Agent: A Controlled Null and Its Mechanism
The research investigates the effectiveness of Reinforcement learning with verifiable rewards and Group Relative Policy Optimization (GRPO) on a small language and vision-language model web agent at scales ranging from…
Knowledge- and Gradient-Guided Reinforcement Learning for Parametrized Action Markov Decision Processes
In this research paper, the authors present a novel reinforcement learning algorithm called Knowledge- and Gradient-Guided Reinforcement Learning (KGRL). This algorithm is designed to work in Parametrized Action Markov…
OOD-RL-Bench: A Benchmark Framework for Out-of-Distribution Detection in Reinforcement Learning
The paper introduces OOD-RL-Bench, a new framework for testing out-of-distribution (OOD) detection in reinforcement learning (RL). The framework is designed to evaluate the performance of detectors against various…
TerraZero: Procedural Driving Simulation for Zero-Demonstration Self-Play at Scale
TerraZero is a procedural driving simulation developed by researchers to train autonomous driving agents efficiently at scale without the need for real-world data demonstrations. It offers improved speed, realistic…
TerraZero: Procedural Driving Simulation for Zero-Demonstration Self-Play at Scale
TerraZero is a procedural driving simulator and training stack for reinforcement learning at scale, designed to train autonomous driving agents without human demonstrations or fallback planners. The simulator provides…
Unveiling Complex Collective Behaviors from Simple Rewards
Researchers have developed a new method called the Agent Response Map (ARM) to help understand how simple rewards can lead to complex behaviors in multi-agent reinforcement learning (MARL). The ARM reveals that robots…
A Minimalist Retargeting-Guided Reinforcement Learning Recipe for Dexterous Manipulation
Researchers introduce REGRIND, a method that enables robots to learn dexterous manipulation policies from a single human demonstration. The method retargets hand-object motion to a robot reference and trains an RL…
Auditing the Risk Claims of Distributional Reinforcement Learning
In a study titled 'Auditing the Risk Claims of Distributional Reinforcement Learning', Hari Prasad investigates whether the risk claims made by distributional reinforcement learning agents are accurate. The study finds…
Learning Residual Kinematic Corrections for Continuous Neural Decoding via Reinforcement Learning
A new method using reinforcement learning (RL) has been proposed to improve the accuracy of decoding continuous three-dimensional motor imagery signals from non-invasive EEG data for brain-computer interfaces (BCIs)…
Learning Residual Kinematic Corrections for Continuous Neural Decoding via Reinforcement Learning
Researchers proposed a new method for improving the accuracy of decoding continuous three-dimensional motor imagery signals using non-invasive EEG-based BCIs. They introduced a two-stage decoding framework that applies…
ADORN: Adaptive Drift handling for Open RAN using Reinforcement Learning
The paper proposes an adaptive retraining method for Open Radio Access Networks (O-RAN) using Reinforcement Learning (RL). The goal is to balance forecasting accuracy and retraining cost in the presence of dynamic…
DrugGen 2: A disease-aware language model for enhancing drug discovery
A new generative model called DrugGen-2 has been developed to enhance drug discovery. It takes into account both disease ontology and target protein sequences, improving upon existing models in generating unique…
Multi-Modal, Multi-Environment Machine Teaching for Robust Reward Learning
The paper proposes a method for machine learning that aims to align autonomous agents' behavior with human intent in diverse operational contexts by using a hierarchical machine teaching algorithm for reward learning…
Self-Adaptive Anomaly Detection with Reinforcement Learning and Human Feedback in Connected Vehicles
The paper proposes an online anomaly detection framework for autonomous cyber-physical systems (CPS), specifically connected vehicles, that combines three coordinated mechanisms: a deep Q-network with self-attention to…
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