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Found 16 papers, 7 papers with code
Absolute Zero: Reinforced Self-play Reasoning with Zero Data
Reinforcement learning with verifiable rewards (RLVR) has shown promise in enhancing the reasoning capabilities of large language models by learning directly from outcome-based rewards.
Does Reinforcement Learning Really Incentivize Reasoning Capacity in LLMs Beyond the Base Model?
Overall, our findings suggest that current RLVR methods have not yet realized the potential of RL to elicit truly novel reasoning abilities in LLMs.
Towards Understanding the Benefit of Multitask Representation Learning in Decision Process
Our work tries to fill the gap by extending the analysis to \textit{unknown non-linear} representations, giving a comprehensive analysis for its mechanism in online and transfer learning setting.
Optimizing Social Media Annotation of HPV Vaccine Skepticism and Misinformation Using Large Language Models: An Experimental Evaluation of In-Context Learning and Fine-Tuning Stance Detection Across Multiple Models
This paper leverages large-language models (LLMs) to experimentally determine optimal strategies for scaling up social media content annotation for stance detection on HPV vaccine-related tweets.
Learning the structure of any Hamiltonian from minimal assumptions
We consider two models of control over the time evolution:~the first has access to time reversal ($t < 0$), enabling an algorithm that outputs an $\epsilon$-accurate classical description of $H$ after querying its dynamics for a total of $\widetilde{\mathcal{O}}(m/\epsilon)$ evolution time.
Training of Scaffolded Language Models with Language Supervision: A Survey
In this survey, we refer to this paradigm as training of scaffolded LMs with language supervision.
Model Surgery: Modulating LLM's Behavior Via Simple Parameter Editing
Furthermore, models modified through SFT and RLHF may deviate from the pretrained models, potentially leading to a degradation in foundational LLM capabilities.
Empowering Interdisciplinary Insights with Dynamic Graph Embedding Trajectories
Our work not only addresses the ongoing challenges in visualizing and analyzing DTDG models but also establishes a foundational framework for future investigations into dynamic graph representation and analysis across various disciplines.
DiveR-CT: Diversity-enhanced Red Teaming Large Language Model Assistants with Relaxing Constraints
Recent advances in large language model assistants have made them indispensable, raising significant concerns over managing their safety.
Exploring Text-to-Motion Generation with Human Preference
This paper presents an exploration of preference learning in text-to-motion generation.
Augmenting Unsupervised Reinforcement Learning with Self-Reference
Our approach achieves state-of-the-art results in terms of Interquartile Mean (IQM) performance and Optimality Gap reduction on the Unsupervised Reinforcement Learning Benchmark for model-free methods, recording an 86% IQM and a 16% Optimality Gap.
Avalon's Game of Thoughts: Battle Against Deception through Recursive Contemplation
This study utilizes the intricate Avalon game as a testbed to explore LLMs' potential in deceptive environments.
ExpeL: LLM Agents Are Experiential Learners
The recent surge in research interest in applying large language models (LLMs) to decision-making tasks has flourished by leveraging the extensive world knowledge embedded in LLMs.
A Mixture of Surprises for Unsupervised Reinforcement Learning
However, both strategies rely on a strong assumption: the entropy of the environment's dynamics is either high or low.
Provable General Function Class Representation Learning in Multitask Bandits and MDPs
While multitask representation learning has become a popular approach in reinforcement learning (RL) to boost the sample efficiency, the theoretical understanding of why and how it works is still limited.
Prevalence and recoverability of syntactic parameters in sparse distributed memories
We propose a new method, based on Sparse Distributed Memory (Kanerva Networks), for studying dependency relations between different syntactic parameters in the Principles and Parameters model of Syntax.
