Aaron Courville

Anirudh Buvanesh

Doctorat - UdeM

Superviseur⋅e principal⋅e :

Laurent Charlin

anirudb1102@gmail.com

abhranil.chandra@gmail.com

Abhranil Chandra

Collaborateur·rice de recherche - University of Waterloo

Maîtrise recherche - Université de Montréal

Juan Duque

Doctorat - UdeM

Doctorat - UdeM

Doctorat - UdeM

Amr Khalifa

Doctorat - UdeM

andrei.nicolicioiu@gmail.com

Samuel Lavoie

Doctorat - UdeM

Zhixuan Lin

Doctorat - UdeM

Ahmed Masry

Collaborateur·rice de recherche - N/A

Doctorat - UdeM

Superviseur⋅e principal⋅e :

Doctorat - UdeM

Superviseur⋅e principal⋅e :

Doctorat - UdeM

Doctorat - UdeM

Co-superviseur⋅e :

Rishabh Agarwal

Andrei Nicolicioiu

Doctorat - UdeM

Google Scholar

Evgenii Nikishin

Collaborateur·rice alumni - UdeM

Superviseur⋅e principal⋅e :

Doctorat - UdeM

Johan Samir Obando Ceron

Doctorat - UdeM

Co-superviseur⋅e :

Maîtrise recherche - UdeM

pichedereck@gmail.com

Khaled Rouissi

Maîtrise recherche - UdeM

Esra'a Saleh

Doctorat - UdeM

Superviseur⋅e principal⋅e :

Glen Berseth

Google Scholar

Vedant Shah

Doctorat - UdeM

Doctorat - UdeM

Yusong Wu

Doctorat - UdeM

Superviseur⋅e principal⋅e :

Anna (Cheng-Zhi) Huang

sujin yun

Doctorat - UdeM

Doctorat - UdeM

Doctorat - UdeM

Co-superviseur⋅e :

Yoshua Bengio

Hattie Zhou

Doctorat - UdeM

Superviseur⋅e principal⋅e :

Hugo Larochelle

Publications

Asynchronous RLHF: Faster and More Efficient Off-Policy RL for Language Models

Shengyi Huang

The dominant paradigm for RLHF is online and on-policy RL: synchronously generating from the large language model (LLM) policy, labelling wi… (voir plus)th a reward model, and learning using feedback on the LLM's own outputs. While performant, this paradigm is computationally inefficient. Inspired by classical deep RL literature, we propose separating generation and learning in RLHF. This enables asynchronous generation of new samples while simultaneously training on old samples, leading to faster training and more compute-optimal scaling. However, asynchronous training relies on an underexplored regime, online but off-policy RLHF: learning on samples from previous iterations of our model. To understand the challenges in this regime, we investigate a fundamental question: how much off-policyness can we tolerate for asynchronous training to speed up learning but maintain performance? Among several RLHF algorithms we tested, we find that online DPO is most robust to off-policy data, and robustness increases with the scale of the policy model. We study further compute optimizations for asynchronous RLHF but find that they come at a performance cost, giving rise to a trade-off. Finally, we verify the scalability of asynchronous RLHF by training LLaMA 3.1 8B on an instruction-following task 40% faster than a synchronous run while matching final performance.

2024-10-23

ArXiv (prépublication)

Stick-breaking Attention

Shawn Tan

Yikang Shen

Songlin Yang

Rameswar Panda

2024-10-23

ArXiv (prépublication)

Stick-breaking Attention

Shawn Tan

Yikang Shen

Songlin Yang

Rameswar Panda

2024-10-23

ArXiv (prépublication)

Stick-breaking Attention

Shawn Tan

Yikang Shen

Songlin Yang

Rameswar Panda

2024-10-23

ArXiv (prépublication)

Stick-breaking Attention

Shawn Tan

Yikang Shen

Songlin Yang

Rameswar Panda

2024-10-23

ArXiv (prépublication)

Stick-breaking Attention

Shawn Tan

Yikang Shen

Songlin Yang

Rameswar Panda

2024-10-23

ArXiv (prépublication)

Faster, More Efficient RLHF through Off-Policy Asynchronous Learning

Shengyi Huang

To achieve state-of-the-art chatbots, large language models are finetuned with reinforcement learning (RL), frequently to optimize human fee… (voir plus)dback (RLHF). This process is computationally expensive and can take weeks. Offline approaches, like DPO, learn on a static dataset and are efficient but not performant. The dominant paradigm, online and on-policy---synchronously generating from the model, labelling with a reward model, and learning on feedback from the model's own outputs---is performant but not efficient. Following prior work in the generall deep RL setting, we propose separating the actor and learner in RLHF. This enables the asynchronously generation of new samples while learning on prior samples, thus leading to overall faster training and better scaling. But this requires a novel regime for RLHF, online but off-policy: learning on samples from a previous version of our model. We ask a fundamental question: how much off-policyness can we tolerate for asynchronous training to speed up learning but maintain performance? We find that a contrastive loss, Online DPO, is most robust to off-policy data and that robustness increases with the scale of the policy model. We show even further compute optimizations but demonstrate that they come at a performance cost, giving rise to a trade-off. Finally, we verify our design choices by training LLaMA 3.1 8B with RLHF as a helpful chatbot in half the time of a synchronous run while matching final performance.

2024-10-10

NeurIPS.cc/2024/Workshop/FITML (poster)

Not All LLM Reasoners Are Created Equal

Daniel Toyama

2024-10-09

NeurIPS.cc/2024/Workshop/Sys2-Reasoning (poster)

VinePPO: Accurate Credit Assignment in RL for LLM Mathematical Reasoning

Amirhossein Kazemnejad

Milad Aghajohari

Large language models (LLMs) are increasingly required to solve complex reasoning tasks, like mathematical problems, that involve multiple r… (voir plus)easoning steps before feedback is received. Effectively identifying and prioritizing key steps by accurately assigning credit to these intermediate steps is essential for enhancing model performance. Proximal Policy Optimization (PPO), a state-of-the-art reinforcement learning algorithm for finetuning LLMs, addresses the credit assignment problem by employing value networks to predict the expected cumulative rewards of intermediate states. In this work, we identify significant limitations with this value estimation method. To address this, we propose \methodname that leverages the flexibility of language environments to compute unbiased Monte Carlo-based estimates of the intermediate values. VinePPO consistently outperforms standard PPO, doing so more efficiently and with lower divergence from the reference model. Our findings underscore the critical importance of accurate credit assignment in LLM post-training and present a simple, yet effective solution.

2024-10-09

NeurIPS.cc/2024/Workshop/MATH-AI (accepté)

VinePPO: Accurate Credit Assignment in RL for LLM Mathematical Reasoning

Amirhossein Kazemnejad

Milad Aghajohari

2024-10-09

NeurIPS.cc/2024/Workshop/MATH-AI (accepté)

Not All LLM Reasoners Are Created Equal

Daniel Toyama

We study the depth of grade-school math (GSM) problem-solving capabilities of LLMs. To this end, we evaluate their performance on pairs of e… (voir plus)xisting math word problems together so that the answer to the second problem depends on correctly answering the first problem. Our findings reveal a significant reasoning gap in most LLMs, that is performance difference between solving the compositional pairs and solving each question independently. This gap is more pronounced in smaller, more cost-efficient, and math-specialized models. Moreover, instruction-tuning recipes and code generation have varying effects across LLM sizes, while finetuning on GSM can lead to task overfitting. Our analysis indicates that large reasoning gaps are not because of test-set leakage, but due to distraction from additional context and poor second-hop reasoning. Overall, LLMs exhibit systematic differences in their reasoning abilities, despite what their performance on standard benchmarks indicates.

2024-10-02

ArXiv (prépublication)

VinePPO: Refining Credit Assignment in RL Training of LLMs

Amirhossein Kazemnejad

Milad Aghajohari

Large language models (LLMs) are increasingly applied to complex reasoning tasks that require executing several complex steps before receivi… (voir plus)ng any reward. Properly assigning credit to these steps is essential for enhancing model performance. Proximal Policy Optimization (PPO), a common reinforcement learning (RL) algorithm used for LLM finetuning, employs value networks to tackle credit assignment. However, recent approaches achieve strong results without it, raising questions about the efficacy of value networks in practice. In this work, we systematically evaluate the efficacy of value networks and reveal their significant shortcomings in reasoning-heavy LLM tasks, showing that they often produce poor estimate of expected return and barely outperform a random baseline when comparing alternative steps. This motivates our key question: Can improved credit assignment enhance RL training for LLMs? To address this, we propose VinePPO, a straightforward approach that leverages the flexibility of language environments to compute unbiased Monte Carlo-based estimates. Our method consistently outperforms PPO and other baselines across MATH and GSM8K datasets in less wall-clock time (up to 3.0x). Crucially, it achieves higher test accuracy for a given training accuracy, capturing more generalization signal per sample. These results emphasize the importance of accurate credit assignment in RL training of LLM.

2024-10-02

ArXiv (prépublication)