TalkRL: The Reinforcement Learning Podcast Robin Ranjit Singh Chauhan

Dr. Vincent Moens is an Applied Machine Learning Research Scientist at Meta, and an author of TorchRL and TensorDict in pytorch. 
Featured References
TorchRL: A data-driven decision-making library for PyTorch Albert Bou, Matteo Bettini, Sebastian Dittert, Vikash Kumar, Shagun Sodhani, Xiaomeng Yang, Gianni De Fabritiis, Vincent Moens 
Additional References  
TorchRL on github  TensorDict Documentation  

Arash Ahmadian is a Researcher at Cohere and Cohere For AI focussed on Preference Training of large language models. He’s also a researcher at the Vector Institute of AI.
Featured Reference
Back to Basics: Revisiting REINFORCE Style Optimization for Learning from Human Feedback in LLMs
Arash Ahmadian, Chris Cremer, Matthias Gallé, Marzieh Fadaee, Julia Kreutzer, Olivier Pietquin, Ahmet Üstün, Sara Hooker

Additional References
Self-Rewarding Language Models, Yuan et al 2024 Reinforcement Learning: An Introduction, Sutton and Barto 1992Learning from Delayed Rewards, Chris Watkins 1989Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning, Williams 1992

Glen Berseth is an assistant professor at the Université de Montréal, a core academic member of the Mila - Quebec AI Institute, a Canada CIFAR AI chair, member l'Institute Courtios, and co-director of the Robotics and Embodied AI Lab (REAL). 
Featured Links 
Reinforcement Learning Conference 
Closing the Gap between TD Learning and Supervised Learning--A Generalisation Point of View Raj Ghugare, Matthieu Geist, Glen Berseth, Benjamin Eysenbach

Ian Osband is a Research scientist at OpenAI (ex DeepMind, Stanford) working on decision making under uncertainty.  
We spoke about: 
- Information theory and RL 
- Exploration, epistemic uncertainty and joint predictions 
- Epistemic Neural Networks and scaling to LLMs 
Featured References 
Reinforcement Learning, Bit by Bit  Xiuyuan Lu, Benjamin Van Roy, Vikranth Dwaracherla, Morteza Ibrahimi, Ian Osband, Zheng Wen 
From Predictions to Decisions: The Importance of Joint Predictive Distributions 
Zheng Wen, Ian Osband, Chao Qin, Xiuyuan Lu, Morteza Ibrahimi, Vikranth Dwaracherla, Mohammad Asghari, Benjamin Van Roy  
 
Epistemic Neural Networks 
Ian Osband, Zheng Wen, Seyed Mohammad Asghari, Vikranth Dwaracherla, Morteza Ibrahimi, Xiuyuan Lu, Benjamin Van Roy  
Approximate Thompson Sampling via Epistemic Neural Networks 
Ian Osband, Zheng Wen, Seyed Mohammad Asghari, Vikranth Dwaracherla, Morteza Ibrahimi, Xiuyuan Lu, Benjamin Van Roy 
  
Additional References  
Thesis defence, Ian Osband Homepage, Ian Osband Epistemic Neural Networks at Stanford RL Forum Behaviour Suite for Reinforcement Learning, Osband et al 2019 Efficient Exploration for LLMs, Dwaracherla et al 2024 

Sharath Chandra Raparthy on In-Context Learning for Sequential Decision Tasks, GFlowNets, and more!  
Sharath Chandra Raparthy is an AI Resident at FAIR at Meta, and did his Master's at Mila.  
Featured Reference  Generalization to New Sequential Decision Making Tasks with In-Context Learning   Sharath Chandra Raparthy , Eric Hambro, Robert Kirk , Mikael Henaff, , Roberta Raileanu  Additional References  
Sharath Chandra Raparthy Homepage  Human-Timescale Adaptation in an Open-Ended Task Space, Adaptive Agent Team 2023Data Distributional Properties Drive Emergent In-Context Learning in Transformers, Chan et al 2022  Decision Transformer: Reinforcement Learning via Sequence Modeling, Chen et al  2021

Pierluca D'Oro and Martin Klissarov on Motif and RLAIF, Noisy Neighborhoods and Return Landscapes, and more!  
Pierluca D'Oro is PhD student at Mila and visiting researcher at Meta.
Martin Klissarov is a PhD student at Mila and McGill and research scientist intern at Meta.  
Featured References 
Motif: Intrinsic Motivation from Artificial Intelligence Feedback  Martin Klissarov*, Pierluca D'Oro*, Shagun Sodhani, Roberta Raileanu, Pierre-Luc Bacon, Pascal Vincent, Amy Zhang, Mikael Henaff  Policy Optimization in a Noisy Neighborhood: On Return Landscapes in Continuous Control  Nate Rahn*, Pierluca D'Oro*, Harley Wiltzer, Pierre-Luc Bacon, Marc G. Bellemare 
To keep doing RL research, stop calling yourself an RL researcher Pierluca D'Oro