Search Results for author:
Found 22 papers, 11 papers with code
Diversity-Rewarded CFG Distillation
By finetuning, we learn model weights with the ability to generate high-quality and diverse outputs, without any inference overhead.
Gemma 2: Improving Open Language Models at a Practical Size
In this work, we introduce Gemma 2, a new addition to the Gemma family of lightweight, state-of-the-art open models, ranging in scale from 2 billion to 27 billion parameters.
BOND: Aligning LLMs with Best-of-N Distillation
Reinforcement learning from human feedback (RLHF) is a key driver of quality and safety in state-of-the-art large language models.
WARP: On the Benefits of Weight Averaged Rewarded Policies
To tackle the trade-off between KL and reward, in this paper we introduce a novel alignment strategy named Weight Averaged Rewarded Policies (WARP).
RecurrentGemma: Moving Past Transformers for Efficient Open Language Models
We introduce RecurrentGemma, a family of open language models which uses Google's novel Griffin architecture.
Gemma: Open Models Based on Gemini Research and Technology
This work introduces Gemma, a family of lightweight, state-of-the art open models built from the research and technology used to create Gemini models.
MusicRL: Aligning Music Generation to Human Preferences
MusicRL is a pretrained autoregressive MusicLM (Agostinelli et al., 2023) model of discrete audio tokens finetuned with reinforcement learning to maximise sequence-level rewards.
Factually Consistent Summarization via Reinforcement Learning with Textual Entailment Feedback
Despite the seeming success of contemporary grounded text generation systems, they often tend to generate factually inconsistent text with respect to their input.
Abstractive Text Summarization
Natural Language Inference
+3
Get Back Here: Robust Imitation by Return-to-Distribution Planning
We consider the Imitation Learning (IL) setup where expert data are not collected on the actual deployment environment but on a different version.
Decoding a Neural Retriever's Latent Space for Query Suggestion
Neural retrieval models have superseded classic bag-of-words methods such as BM25 as the retrieval framework of choice.
vec2text with Round-Trip Translations
We propose a simple data augmentation technique based on round-trip translations and show in extensive experiments that the resulting vec2text model surprisingly leads to vector spaces that fulfill our four desired properties and that this model strongly outperforms both standard and denoising auto-encoders.
Learning in Mean Field Games: A Survey
Non-cooperative and cooperative games with a very large number of players have many applications but remain generally intractable when the number of players increases.
Scalable Deep Reinforcement Learning Algorithms for Mean Field Games
One limiting factor to further scale up using RL is that existing algorithms to solve MFGs require the mixing of approximated quantities such as strategies or $q$-values.
RLDS: an Ecosystem to Generate, Share and Use Datasets in Reinforcement Learning
We introduce RLDS (Reinforcement Learning Datasets), an ecosystem for recording, replaying, manipulating, annotating and sharing data in the context of Sequential Decision Making (SDM) including Reinforcement Learning (RL), Learning from Demonstrations, Offline RL or Imitation Learning.
Continuous Control with Action Quantization from Demonstrations
The proposed approach consists in learning a discretization of continuous action spaces from human demonstrations.
Brax -- A Differentiable Physics Engine for Large Scale Rigid Body Simulation
We present Brax, an open source library for rigid body simulation with a focus on performance and parallelism on accelerators, written in JAX.
What Matters for Adversarial Imitation Learning?
To tackle this issue, we implement more than 50 of these choices in a generic adversarial imitation learning framework and investigate their impacts in a large-scale study (>500k trained agents) with both synthetic and human-generated demonstrations.
Hyperparameter Selection for Imitation Learning
The vast literature in imitation learning mostly considers this reward function to be available for HP selection, but this is not a realistic setting.
What Matters for On-Policy Deep Actor-Critic Methods? A Large-Scale Study
In recent years, reinforcement learning (RL) has been successfully applied to many different continuous control tasks.
What Matters In On-Policy Reinforcement Learning? A Large-Scale Empirical Study
In recent years, on-policy reinforcement learning (RL) has been successfully applied to many different continuous control tasks.
Acme: A Research Framework for Distributed Reinforcement Learning
These implementations serve both as a validation of our design decisions as well as an important contribution to reproducibility in RL research.
