Offline RL for Natural Language Generation with Implicit Language Q Learning

By Charles Burton Snell, Ilya Kostrikov, Mengjiao Yang, Sergey Levine. 2022

154 citations recorded by the source corpus.

Large language models distill broad knowledge from text corpora. However, they can be inconsistent when it comes to completing user specified tasks. This issue can be addressed by finetuning such models via supervised learning on curated datasets, or via reinforcement learning. In this work, we propose a novel offline RL method, implicit language Q-learning (ILQL), designed for use on language models, that combines both the flexible utility maximization framework of RL algorithms with the ability of supervised learning to leverage previously collected data, as well as its simplicity and stability. Our method employs a combination of value conservatism alongside an implicit dataset support constraint in learning value functions, which are then used to guide language model generations towards maximizing user-specified utility functions. In addition to empirically validating ILQL, we present a detailed empirical analysis of situations where offline RL can be useful in natural language generation settings, demonstrating how it can be a more effective utility optimizer than prior approaches for end-to-end dialogue, and how it can effectively optimize high variance reward functions based on subjective judgement, such as whether to label a comment as toxic or not.

Subfields: Artificial Intelligence, Natural Language Processing, Reinforcement Learning

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Citations in the corpus

Pretraining Language Models with Human Preferences (2023, methodology): “However, offline RL techniques were recently used for finetuning pretrained LMs to be aligned with human preferences in dialog tasks (Jaques et al., 2020; Jang et al., 2022; Snell et al., 2022).”
Augmented Language Models: a Survey (2023, background): “Recent works (Zelikman et al., 2022; Snell et al., 2022) have shown that such approaches could reach performance that goes beyond that of the expert demonstrations or improve over initial model generations.”
Is Reinforcement Learning (Not) for Natural Language Processing?: Benchmarks, Baselines, and Building Blocks for Natural Language Policy Optimization (2022, neutral): “…et al., 2020; Paulus et al., 2017), dialogue (Li et al., 2016; Zhou et al., 2017; Jaques et al., 2020), image captioning (Rennie et al., 2017), question generation (Pang & He, 2021), text-games (Narasimhan et al., 2015; Hausknecht et al., 2020), and more (Ranzato et al., 2016; Snell et al., 2022).”
Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback (2023, unknown): “This last setting shares similarities with offline RL, which focuses on training an optimal policy using demonstrations annotated with rewards [Levine et al., 2020, Snell et al., 2022].”
AlpacaFarm: A Simulation Framework for Methods that Learn from Human Feedback (2023, methodology): “RL research applied to NLP has a long history [73, 60, 27, 48, 41, 30, 29, 51, 59], and we expect future work in this direction to benefit from the ideas and artifacts in AlpacaFarm.”
Preference Ranking Optimization for Human Alignment (2023, methodology): “In contrast, ILQL is applicable for the ofﬂine setting, which is released by Snell et al. (2022).”
Reinforcement Learning for Language Models (2023, background): “However, in certain settings, off-policy RL may be more sample efficient, as this approach allows for reusing datasets collected from prior interaction with a potentially different behavior policy [4].”
Understanding the Effects of RLHF on LLM Generalisation and Diversity (2023, methodology): “Most previous work on RLHF uses on-policy RL algorithms such as Proximal Policy Optimization (PPO) (Schulman et al., 2017) or Advantage Actor Critic (A2C) (Mnih et al., 2016), but offline RL methods have also been proposed (Snell et al., 2022).”
Visual-RFT: Visual Reinforcement Fine-Tuning (2025, unknown): “While there has been significant research on using reinforcement learning to enhance LLMs during post-training [1, 3, 27, 30, 34, 35, 38, 44, 48, 62, 63], the progress for LVLMs has been slower.”
Training Language Models to Self-Correct via Reinforcement Learning (2024, unknown): “The most prominent settings include problems where external input tokens from an environment are available, such as agentic tasks (Liu et al., 2023), code repair (Jain et al., 2024), and tool use (Chen et al., 2023).”