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Offline RL for Natural Language Generation with Implicit Language Q Learning

Charlie Snell, Ilya Kostrikov, Yi Su, Mengjiao Yang, Sergey Levine

arXiv:2206.11871v2cs.CLcs.LG

TL;DR

Language models know substantial text-derived information but are inconsistent at user-specified tasks, motivating methods that combine supervised data use with reward optimization. ILQL is an offline, token-level RL method that learns conservative, dataset-supported value functions to guide generation. Across dialogue and subjective-reward settings, the paper reports effective utility optimization, while noting limitations on highly suboptimal data and distributionally constrained applications.

  • Problem

    Language models are not designed to optimize user-specified utilities, while existing approaches may require curated data or costly interaction.

  • Method

    ILQL combines offline RL with supervised data reuse, conservative value learning, an implicit dataset support constraint, and value-guided generation.

  • Results

    ILQL adapts across diverse utility functions, outperforms baselines on many Visual Dialogue rewards, and effectively optimizes subjective rewards such as toxicity.

  • Takeaways & Limitations

    Offline RL can serve as a strong alternative to filtered fine-tuning and classifier guidance for controllable language generation.

  • Takeaways & Limitations

    ILQL may be ineffective on highly suboptimal datasets and is not ideal when distributional constraints such as fairness are required.

Abstract

from arXiv · show

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.

1 INTRODUCTION

Large language models encode broad knowledge but are cumbersome to steer toward user-specified utilities. ILQL addresses this with offline RL that uses existing suboptimal data while optimizing rewards and supporting stable, practical language-model control.

  • 1 INTRODUCTION: Large language models learn from text corpora but are not designed to optimize user-specified utility, often requiring trial-and-error prompt engineering.Standard unsupervised training is effective for acquiring knowledge but less effective for applying it to specified tasks.
  • 1 INTRODUCTION: Offline RL combines supervised learning’s use of existing data with RL’s ability to optimize rewards and exploit temporal compositionality.This combination targets practical requirements including reward flexibility, interactive tasks, and improvement beyond average dataset behavior.
  • 1 INTRODUCTION: ILQL is an offline RL algorithm designed to learn high-performing language-model policies from suboptimal data while optimizing arbitrary user-specified rewards.The paper presents ILQL as a stable optimization process intended to satisfy practical NLP RL requirements.
  • 1 INTRODUCTION: ILQL’s empirical analysis covers adaptation to diverse utility functions and optimization of stochastic or subjective rewards such as toxicity judgments.The authors specifically report fewer toxic outputs than supervised fine-tuning on only non-toxic comments.

2 RELATED WORK

Prior language-generation RL methods can be impractical for interactive tasks or insufficiently simple and stable. ILQL instead performs self-contained, per-token offline RL using dataset support constraints and a policy extraction method suited to language models.

  • 2 RELATED WORK: Offline RL removes environment interaction and user simulators by learning solely from static datasets of prior human interaction.This directly addresses the impracticality of repeated human interaction in multi-step language tasks such as dialogue.
  • 2 RELATED WORK: Earlier NLP offline RL methods based on approximate dynamic programming lacked supervised-learning-level simplicity, stability, and ease of use.Per-utterance action spaces can make training-time decoding expensive and increase systems complexity.
  • 2 RELATED WORK: ILQL operates at the per-token level and trains fully self-contained, without simulating generation or querying likelihoods from a separate language model.It combines an implicit dataset support constraint with a policy extraction method designed for discrete token actions.
  • 2 RELATED WORK: The paper positions classifier guidance and curated fine-tuning as closely related policy-extraction approaches that steer language models without explicit utility functions.These methods are discussed alongside offline RL approaches for desired textual attributes.

3 PRELIMINARIES: LANGUAGE GENERATION AS A REINFORCEMENT LEARNING TASK

The paper formulates language generation as token-level partially observable decision making and builds on value-based offline RL constrained to dataset-supported actions. ILQL uses sequence histories and value-based inference to guide generation toward utility-maximizing behavior.

  • 3 PRELIMINARIES: LANGUAGE GENERATION AS A REINFORCEMENT LEARNING TASK: Language generation is modeled as a token-level POMDP in which observations are token histories and actions are next vocabulary tokens.The action space includes a special end-of-turn token.
  • 3 PRELIMINARIES: LANGUAGE GENERATION AS A REINFORCEMENT LEARNING TASK: Value-based offline RL learns a policy from a static dataset produced by potentially suboptimal behavior while constraining Bellman optimization to in-dataset actions.ILQL builds on implicit Q-learning, which approximates this constrained optimization with expectile regression.
  • 3 PRELIMINARIES: LANGUAGE GENERATION AS A REINFORCEMENT LEARNING TASK: ILQL training uses a supervised behavior model, a Q/V value model, and a Polyak-averaged target value network.At inference, learned values perturb the behavior model’s token log probabilities toward utility-maximizing behavior.
  • 3 PRELIMINARIES: LANGUAGE GENERATION AS A REINFORCEMENT LEARNING TASK: The supervised baseline terminology distinguishes filtered fine-tuning on curated or filtered data from fine-tuning on unfiltered data.The paper uses these baselines to align reinforcement-learning comparisons with common NLP terminology.

4 IMPLICIT LANGUAGE Q-LEARNING

ILQL adapts implicit Q-learning to token-level language generation by combining value learning with behavior-policy-based policy extraction. Its design targets stable, efficient optimization while constraining learned values to dataset-supported behavior.

  • Method: ILQL extends IQL to language-model POMDPs with sequence-level histories, value conservatism, and token-level policy extraction.The method handles partial observability by predicting values from full token histories and adds a conservatism loss to address out-of-distribution Q-values.
  • Value learning: Recursive Q- and V-function fitting performs iterative policy improvement under an implicit dataset-support constraint set by the expectile.The value and Q functions bootstrap through Bellman backups while the expectile controls the support constraint.
  • Policy extraction: The implicit policy perturbs the behavior policy using learned Q and V values, avoiding training a separate actor.This preserves a standard supervised-finetuning objective for the behavioral model while incorporating value-based improvement at decoding time.
  • Value conservatism: CQL regularization alone requires careful weight tuning, but combining it with the behavior policy makes small α values typically effective.The behavior-policy component reduces sensitivity to the CQL weight during decoding.
  • Implementation: ILQL uses two Q heads and a V head, with the target Q defined as the minimum of the two Q predictions.Causal masking enables Bellman updates over complete sequences in parallel.

5 PROOF OF CONCEPT: MULTI-STEP OFFLINE RL ON WORDLE

The Wordle benchmark is designed to test whether offline RL can perform multiple policy-improvement steps in sequential language-like decision making. ILQL recovers policies that outperform single-step methods on both synthetic and natural Wordle data.

  • Motivation: Multi-step value updates can recover an optimal action sequence when optimal trajectories pass through states also common in sub-optimal data.Single-step methods instead favor actions that appear safer according to the dataset distribution.
  • Benchmark design: Wordle provides objective simulated evaluation while retaining sequential, turn-based, and non-deterministic properties relevant to dialogue.The benchmark uses real human game data and permits datasets with different properties for comparing offline RL methods.
  • Synthetic Wordle: ILQL shows the opposite action preference from single-step RL in the synthetic Wordle setting, supporting multi-step policy improvement.The synthetic dataset deliberately combines policies representing different paths through the underlying MDP.
  • Natural Wordle data: ILQL outperforms single-step RL on a natural dataset of Wordle games scraped from Twitter.This transfers the synthetic finding to a more realistic dataset rather than limiting the comparison to a constructed MDP.

6 NATURAL LANGUAGE EXPERIMENTS

Across dialogue and Reddit generation, ILQL is evaluated as an offline optimizer for diverse rewards, sub-optimal data, and noisy subjective judgments. It generally outperforms baselines while producing reward-specific behavior and maintaining favorable stability and inference efficiency.

  • Noisy rewards: ILQL consistently outperforms filtered-data finetuning on the Reddit tasks, where filtering high-reward examples can fail under stochastic and subjective rewards.Table 2 reports that ILQL never generates undesirable comments on two of three rewards, whereas filtered finetuning occasionally does.
  • Visual Dialogue: ILQL outperforms baselines on most Visual Dialogue reward functions, including rewards for which the dataset is sub-optimal.Agents trained for different rewards learn distinct behaviors and generally perform worse when evaluated on rewards they were not trained for.
  • Reddit comments: On Reddit rewards, ILQL is evaluated on toxicity and predicted positive upvotes using four million comments and noisy, subjective reward signals.The toxicity reward distinguishes toxic, moderately toxic, and non-toxic comments, while the upvote reward favors comments predicted to receive positive votes.
  • Offline RL comparison: ILQL significantly outperforms other offline RL methods on the Visual Dialogue “y/n” task while showing the second-lowest hyperparameter variance.The comparison includes per-token CQL, ψ-learning, decision transformer, and single-step RL.
  • Ablations: The ablations examine token-level actions, value learning, and policy extraction to identify components contributing to ILQL’s performance and usability.The policy-extraction analysis reports that AWR and GOLD require additional training-time hyperparameters, reducing flexibility.
  • Ablations: ILQL outperforms per-utterance ILQL and single-step RL while running inference approximately 4x faster on a single T4 GPU.CHAI can perform similarly when tuned well, but is less stable and more than twice as slow at inference.

7 CONCLUSION

The paper presents ILQL as an offline RL method for steering language generation toward desirable conversational behaviors across varied tasks. It reports that offline RL can complement supervised fine-tuning and classifier guidance, while requiring greater computational cost.

  • ILQL steers language generation toward desirable conversational behaviors across diverse tasks.Experiments include word games, goal-directed question asking, optimizing upvotes, and minimizing toxic language.
  • Offline RL with ILQL is presented as a strong alternative to manually filtered supervised fine-tuning and classifier guidance.
  • ILQL requires 2x more training time than standard supervised learning because it uses three transformer networks during training and two during inference.

8 ETHICS STATEMENT

The ethics statement emphasizes that utility optimization can be used for either beneficial or harmful purposes. It also identifies dataset quality and distributional constraints such as fairness as practical limitations.

  • Utility optimization methods can be used to aid or harm, so ethical uses of offline RL require consideration.
  • ILQL may be ineffective when datasets are highly suboptimal.
  • Offline RL is not ideal for settings requiring distributional constraints, such as fairness.

9 REPRODUCIBILITY STATEMENT

The paper supports reproducibility by documenting baseline hyperparameter settings, experimental details, and source code for reproducing the experiments.

  • The appendix reports extensive results for all hyperparameter settings tried for every baseline.
  • The paper describes its experimental details to support reproducibility.
  • Supplemental source code is provided for reproducing all experiments.

A.1 JUSTIFICATIONS FOR OFFLINE RL IN DIALOGUE

Dialogue is a rich, interactive sequential setting that is well suited to offline RL’s utility-optimization framework. Offline RL avoids the repeated real-world interaction or realistic simulation required by online RL for realistic dialogue tasks.

  • Dialogue combines rich interaction with sequential decision making, making it an appropriate target for offline RL.
  • RL offers a utility-optimization framework for sequential settings such as dialogue.
  • Online RL for realistic dialogue requires repeated real-world interaction or a realistic environment simulator.

A.2 TEMPORAL COMPOSITIONALITY

Value-function-based reinforcement learning can perform temporal compositionality, or “stitching,” by combining locally optimal parts of suboptimal trajectories into globally optimal behavior.

  • Temporal compositionality lets value-function methods stitch locally optimal parts of suboptimal trajectories into globally optimal behavior.This can distill optimal behavior from datasets containing only suboptimal demonstrations.

A.3 FULL TOKEN-LEVEL POMDP FORMULATION

The paper formalizes language tasks as token-level decision processes and evaluates ILQL across dialogue, Wordle, noisy rewards, and controllable output diversity. These experiments examine policy improvement, reward uncertainty, and the trade-off between optimization and diversity.

  • A.3 FULL TOKEN-LEVEL POMDP FORMULATION: Token-level actions select the next vocabulary token, while dialogue transitions append tokens until an end-of-turn action yields control to the other speaker.The formulation represents observations as token histories and policies as mappings from histories to action distributions.
  • A.4 GENERAL EXPERIMENT DETAILS: The experimental setup uses GPT-2 architectures with separate policy, Q/V, and target-Q transformers, generally initialized from pretrained weights.Wordle instead uses random initialization and a character-plus-color token set; experiments run on one GPU for at most 72 V100 hours.
  • A.5 WORDLE TASK DETAILS: On realistic Twitter Wordle data, ILQL’s multiple policy-improvement steps outperform methods using only a single improvement step.The benchmark uses human gameplay data while retaining simulated objective evaluation.
  • A.6 VISUAL DIALOGUE TASK DETAILS: Visual Dialogue rewards agents when questions improve the ground-truth image’s percentile ranking enough to end the episode successfully.The reward is based on relative ranking among evaluation-set images and encourages effective question asking.
  • A.8 NOISY REWARDS: Offline RL outperforms filtered fine-tuning on noisy toxicity tasks by representing reward uncertainty and avoiding outputs with non-trivial undesirable-outcome probability.The authors attribute the advantage to Q-learning relating actions to expected rewards rather than imitating only curated high-reward examples.
  • A.9 TRADING OFF OUTPUT DIVERSITY FOR OPTIMIZATION: Increasing β generally improves performance but lowers entropy, allowing inference-time control over the trade-off between optimization and language diversity.The policy becomes more influenced by the Q-function as β increases and approaches the data distribution as β decreases.
  • A.12 REDDIT COMMENTS PRELIMINARY USER STUDY: Human ratings aligned with automatic toxicity evaluations, with ILQL and single-step RL never generating toxic comments in the reported study.For diversity, β=4 was judged more diverse than β=32 in 282 of 289 comparisons.
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