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Found 19 papers, 6 papers with code
ShiQ: Bringing back Bellman to LLMs
The fine-tuning of pre-trained large language models (LLMs) using reinforcement learning (RL) is generally formulated as direct policy optimization.
Command A: An Enterprise-Ready Large Language Model
In this report we describe the development of Command A, a powerful large language model purpose-built to excel at real-world enterprise use cases.
Overconfident Oracles: Limitations of In Silico Sequence Design Benchmarking
Machine learning methods can automate the in silico design of biological sequences, aiming to reduce costs and accelerate medical research.
Aya Expanse: Combining Research Breakthroughs for a New Multilingual Frontier
We introduce the Aya Expanse model family, a new generation of 8B and 32B parameter multilingual language models, aiming to address the critical challenge of developing highly performant multilingual models that match or surpass the capabilities of monolingual models.
Averaging log-likelihoods in direct alignment
To reconcile these approaches, we introduce a principled approach for making direct alignment length-invariant.
Contrastive Policy Gradient: Aligning LLMs on sequence-level scores in a supervised-friendly fashion
Reinforcement Learning (RL) has been used to finetune Large Language Models (LLMs) using a reward model trained from preference data, to better align with human judgment.
Memory-Enhanced Neural Solvers for Efficient Adaptation in Combinatorial Optimization
We successfully train all RL auto-regressive solvers on large instances, and show that MEMENTO can scale and is data-efficient.
Should we be going MAD? A Look at Multi-Agent Debate Strategies for LLMs
In this context, multi-agent debate (MAD) has emerged as a promising strategy for enhancing the truthfulness of LLMs.
Combinatorial Optimization with Policy Adaptation using Latent Space Search
Combinatorial Optimization underpins many real-world applications and yet, designing performant algorithms to solve these complex, typically NP-hard, problems remains a significant research challenge.
Jumanji: a Diverse Suite of Scalable Reinforcement Learning Environments in JAX
Open-source reinforcement learning (RL) environments have played a crucial role in driving progress in the development of AI algorithms.
Winner Takes It All: Training Performant RL Populations for Combinatorial Optimization
Applying reinforcement learning (RL) to combinatorial optimization problems is attractive as it removes the need for expert knowledge or pre-solved instances.
Meta-learning from Learning Curves Challenge: Lessons learned from the First Round and Design of the Second Round
With the lessons learned from the first round and the feedback from the participants, we have designed the second round of our challenge with a new protocol and a new meta-dataset.
More Efficient Exploration with Symbolic Priors on Action Sequence Equivalences
Incorporating prior knowledge in reinforcement learning algorithms is mainly an open question.
Better state exploration using action sequence equivalence
Incorporating prior knowledge in reinforcement learning algorithms is mainly an open question.
There Is No Turning Back: A Self-Supervised Approach for Reversibility-Aware Reinforcement Learning
We propose to learn to distinguish reversible from irreversible actions for better informed decision-making in Reinforcement Learning (RL).
Low-Rank Projections of GCNs Laplacian
In this work, we study the behavior of standard models for community detection under spectral manipulations.
Interferometric Graph Transform for Community Labeling
We present a new approach for learning unsupervised node representations in community graphs.
A spectral perspective on GCNs
In this work, we study the behavior of standard GCNs under spectral manipulations.
Geometric Deep Reinforcement Learning for Dynamic DAG Scheduling
In this paper, we propose a reinforcement learning approach to solve a realistic scheduling problem, and apply it to an algorithm commonly executed in the high performance computing community, the Cholesky factorization.
