20 citations · 55 across the 11 of their papers we have counts for
12 papers
Recursive Training Loops in LLMs: How training data properties modulate distribution shift in generated data?
Grgur Kovač, Jérémy Perez, Rémy Portelas +2
Large language models (LLMs) are increasingly used in the creation of online content, creating feedback loops as subsequent generations of models will be trained on this synthetic…
Stick to your Role! Stability of Personal Values Expressed in Large Language Models
Grgur Kovač, Rémy Portelas, Masataka Sawayama +2
The standard way to study Large Language Models (LLMs) with benchmarks or psychology questionnaires is to provide many different queries from similar minimal contexts (e.g. multipl…
The SocialAI School: Insights from Developmental Psychology Towards Artificial Socio-Cultural Agents
Grgur Kovač, Rémy Portelas, Peter Ford Dominey +1
Developmental psychologists have long-established the importance of socio-cognitive abilities in human intelligence. These abilities enable us to enter, participate and benefit fro…
Large Language Models as Superpositions of Cultural Perspectives
Grgur Kovač, Masataka Sawayama, Rémy Portelas +3
Large Language Models (LLMs) are often misleadingly recognized as having a personality or a set of values. We argue that an LLM can be seen as a superposition of perspectives with…
SocialAI: Benchmarking Socio-Cognitive Abilities in Deep Reinforcement Learning Agents
Grgur Kovač, Rémy Portelas, Katja Hofmann +1
Building embodied autonomous agents capable of participating in social interactions with humans is one of the main challenges in AI. Within the Deep Reinforcement Learning (DRL) fi…
SocialAI 0.1: Towards a Benchmark to Stimulate Research on Socio-Cognitive Abilities in Deep Reinforcement Learning Agents
Grgur Kovač, Rémy Portelas, Katja Hofmann +1
Building embodied autonomous agents capable of participating in social interactions with humans is one of the main challenges in AI. This problem motivated many research directions…
TeachMyAgent: a Benchmark for Automatic Curriculum Learning in Deep RL
Clément Romac, Rémy Portelas, Katja Hofmann +1
Training autonomous agents able to generalize to multiple tasks is a key target of Deep Reinforcement Learning (DRL) research. In parallel to improving DRL algorithms themselves, A…
Meta Automatic Curriculum Learning
Rémy Portelas, Clément Romac, Katja Hofmann +1
A major challenge in the Deep RL (DRL) community is to train agents able to generalize their control policy over situations never seen in training. Training on diverse tasks has be…
Trying AGAIN instead of Trying Longer: Prior Learning for Automatic Curriculum Learning
Rémy Portelas, Katja Hofmann, Pierre-Yves Oudeyer
A major challenge in the Deep RL (DRL) community is to train agents able to generalize over unseen situations, which is often approached by training them on a diversity of tasks (o…
Automatic Curriculum Learning For Deep RL: A Short Survey
Rémy Portelas, Cédric Colas, Lilian Weng +2
Automatic Curriculum Learning (ACL) has become a cornerstone of recent successes in Deep Reinforcement Learning (DRL).These methods shape the learning trajectories of agents by cha…
Language Grounding through Social Interactions and Curiosity-Driven Multi-Goal Learning
Nicolas Lair, Cédric Colas, Rémy Portelas +3
Autonomous reinforcement learning agents, like children, do not have access to predefined goals and reward functions. They must discover potential goals, learn their own reward fun…
Teacher algorithms for curriculum learning of Deep RL in continuously parameterized environments
Rémy Portelas, Cédric Colas, Katja Hofmann +1
We consider the problem of how a teacher algorithm can enable an unknown Deep Reinforcement Learning (DRL) student to become good at a skill over a wide range of diverse environmen…