Summary Reinforced Self-Training for Language Modeling arxiv.org
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Reinforced Self-Training (ReST) improves large language models (LLMs) by aligning them with human preferences through a combination of initial LLM policy generation and offline reinforcement learning (RL) algorithms.
Slides
Slide Presentation (10 slides)
Key Points
- Reinforced Self-Training (ReST) is a method for aligning large language models (LLMs) with human preferences.
- ReST involves generating a dataset using an initial LLM policy and using offline reinforcement learning (RL) algorithms to improve the model.
- Different variants of ReST outperform supervised learning in language modeling.
- The best loss function for ReST is BC loss.
- The document includes references to papers and resources related to reinforcement learning and language modeling, including those published by DeepMind.
- The document also mentions the use of reference-free reward models called Metric X to evaluate translations.
- The document discusses the vulnerability of reference-free reward models to distribution shifts and reward hacking.
- The document references previous work on unsupervised word sense disambiguation and the use of Launchpad for distributed machine learning research.
Summaries
29 word summary
Reinforced Self-Training (ReST) aligns large language models (LLMs) with human preferences by generating a dataset using an initial LLM policy and improving it with offline reinforcement learning (RL) algorithms.
35 word summary
Reinforced Self-Training (ReST) is a method for aligning large language models (LLMs) with human preferences. It involves generating a dataset using an initial LLM policy and using offline reinforcement learning (RL) algorithms to improve the
478 word summary
Reinforced Self-Training (ReST) is a method for aligning large language models (LLMs) with human preferences. It involves generating a dataset using an initial LLM policy and using offline reinforcement learning (RL) algorithms to improve the
The proposed Reinforced Self-Training (ReST) approach for language modeling is simple, stable, and has a small number of hyperparameters. The approach involves training a model on a dataset using the negative log likelihood (NLL) loss. The
The excerpt discusses a method called Reinforced Self-Training (ReST) for language modeling. The algorithm involves finetuning the current best policy using either supervised learning or offline reinforcement learning. ReST includes a "Grow" step that allows the model
We conducted experiments on different language pairs to test the generality of our results. We used a reference-free reward model called Metric X to evaluate the proposed translations. The results were reported in terms of average rewards on the validation set. We named the variants
Different variants of Reinforced Self-Training (ReST) significantly outperform supervised learning in language modeling, even after just the first grow step. The best loss function for ReST is BC loss, which outperforms other loss functions. ReST
The document is a compilation of references to various papers and resources related to reinforcement learning and language modeling. It includes references to papers published by DeepMind, as well as other researchers and organizations. The cited papers cover topics such as training language models using reinforcement
This text excerpt includes a list of references to various papers and conference proceedings related to machine learning, deep reinforcement learning, language modeling, and neural metrics. These references span from 2018 to 2023 and cover topics such as scalable distributed deep-
This summary provides a list of references from various research papers related to language modeling and reinforcement learning. The papers mentioned include topics such as parallel corpus filtering and alignment, subword regularization, offline reinforcement learning, batch reinforcement learning, machine translation decoding, competition-level
This document is a summary of several research papers related to language modeling. The papers mentioned cover a range of topics including red teaming language models, autonomous land vehicles using neural networks, scaling language models, direct preference optimization, neural framework for machine translation evaluation
This text excerpt includes a list of references to various papers and articles related to language modeling and self-training. The references cover topics such as policy optimization, capabilities of language models, summarization with human feedback, neural machine translation, sequence-to-sequence learning
The document discusses the Reinforced Self-Training (ReST) approach for language modeling. It introduces a programming model called Launchpad for distributed machine learning research. The document also references previous work on unsupervised word sense disambiguation and the
In our experiments, we used reference-free reward models, which are vulnerable to distribution shifts and reward hacking. We pre-computed and stored rewards for generated data and conducted unit tests to ensure high-quality rewards. However, the reward model still showed signs of