LLMR: Knowledge Distillation with a Large Language Model-Induced Reward

arXiv:2409.12500 · cs.CL, cs.AI · Submitted 2024-09-19 · Read on arXiv

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Introduction to the show: ident: AI Radio. Generated commentary on the latest Artificial Intelligence papers.

Tom: Today's paper: "LLMR: Knowledge Distillation with a Large Language Model-Induced Reward".

Jane: The gist: Large language models have become increasingly popular and demonstrated remarkable performance in various natural language processing (NLP) tasks,

Tom: First, who's behind it and why it matters.

Paper summary: Tom: So looking at the paper "LLMR: Knowledge Distillation with a Large Language Model-Induced Reward," it seems like they’ve successfully used reinforcement learning to create a more robust way to transfer knowledge from large language models into smaller student models >

Jane: It’s interesting how they connected the concept of inducing q-value functions from the LLM's next-word probability directly into a reward function that guides the distillation process >

Lu: The implication is that instead of relying on just matching outputs or some loss function, we can use this RL structure to actively encourage the student model to explore different sequences during distillation >

Meng: For production, it means if we can set up a system where the reward is calculated this way, we might get a better distilled model that handles out-of-distribution data more gracefully >

Lalam: The LLMR method directly addresses the exposure bias issue that plagues traditional knowledge distillation by allowing the student to explore based on its own generated sequences during training >

Tom: So the authors argue that this approach is solid because it shows concrete improvements across different metrics and datasets compared to SeqKD, KL, RKL, and JS methods >

Jane: It really boils down to using a large language model's internal scoring mechanism as the engine for teaching a smaller model how to generate good text >

Conclusion: Tom: So we're wrapping up this look at LLMR, Knowledge Distillation with a Large Language Model-Induced Reward. Basically, they took a big language model and used its own way of scoring words to teach a smaller model how to generate text better.

Jane: It’s about taking the complex knowledge locked inside a giant model and making it accessible to something much smaller without losing that quality. The authors are Toufique Ahmed and Premkumar Devanbu.

Lu: I found their method interesting because they aren't just copying the output directly, which is what most distillation does. They’re inducing a reward function based on that LLM's next-word probabilities.

Meng: From a deployment standpoint, I’m curious about how much overhead this reward induction adds to the training pipeline for a production system. Does it become too computationally heavy?

Lalam: I see what they did here because it avoids the exposure bias problem that plagues standard methods. The student model gets to explore its own path during training guided by this LLM-induced signal, which is much safer than just following the teacher's single guess.

Tom: Yeah, and looking at their results on dialogue generation and summarization, they’re showing consistent wins over traditional knowledge distillation techniques like SeqKD. It looks solid on those benchmarks.

Jane: So what does this mean for people actually using these smaller models? It suggests we can build more capable, less expensive tools without having to train massive models from scratch every single time.

Lu: The implication is that we can start distilling knowledge from huge models into something much lighter and still high-performing for specific tasks.

Meng: I’m still wary of the scalability, though. If inducing this reward function requires querying the LLM step-by-step during training, the training time could get pretty long at scale.

Lalam: It’s about making that trade-off between model size and performance manageable by using this reinforcement learning approach for distillation.

Tom: The key thing here is that they've shown it works across different datasets, which makes it less of a one-off trick and more of a consistent technique.

Jane: So the big picture is moving toward building specialized, high-quality AI components that aren't as resource hungry as the behemoths we see today. (Music swells slightly)

Department of Computing Science and Alberta Machine Intelligence Institute, University of Alberta

cs.CL, cs.AI

Submitted: 2024-09-19

Updated: 2026-10-15

Comments: Accepted by LREC COLING 2024

Journal ref: Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 10657-10664

Code: https://github.com/MANGA-UOFA/Prompt-LLMR

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Importance score: 90/100

The gist: The gist: Large language models have become increasingly popular and demonstrated remarkable performance in various natural language processing (NLP) tasks, but they are typically computationally

Key concepts

Knowledge Distillation (KD): KD transfers knowledge from a large 'teacher' model to a smaller 'student' model. This helps the student learn complex patterns without needing massive resources. It traditionally involves matching the teacher's outputs, but this often causes performance drops due to exposure bias.
LLM-Induced Reward: This method creates a specific reward signal by querying an LLM step-by-step. This process derives a dense reward function that evaluates the appropriateness of each word in a sequence based on the LLM's next-word probabilities, providing richer feedback than simple end-of-sequence rewards.
Reinforcement Learning (RL) Based KD: Instead of just using teacher predictions, this approach uses RL to train the student. The student samples sequences and gets a reward from the LLM-induced function. This trial-and-error learning allows the student to explore its own predictions, effectively mitigating exposure bias.

Terminology

Distillation — 2× in this paper · explained in 2 episodes
Knowledge Distillation — 2× in this paper · explained in 12 episodes
Reward Function — 1× in this paper · explained in 3 episodes

Summary

The gist: Large language models have become increasingly popular and demonstrated remarkable performance in various natural language processing (NLP) tasks, but they are typically computationally expensive and difficult to be deployed in resource-constrained environments, leading to the proposal of LLMR as a novel knowledge distillation method based on a reward function induced from large language models.

Knowledge Distillation Background

Knowledge distillation (KD) aims to transfer the knowledge of a teacher model to a student model, which is important because it reduces the computing and memory demands of large neural networks while retaining high performance. Common KD approaches include matching output distributions and matching intermediate-layer representations. Conventionally, this is accomplished by training the student from the teacher’s predicted sentences or distributions. However, it has inherent limitations because during training, the student learns to predict the next word based on the teacher’s previous predictions, whereas during inference, the student has to do so based on its own previous predictions. Such a discrepancy is known as exposure bias and often leads to a performance degradation.

LLMR Approach Overview

In this paper, the authors propose LLMR, a novel knowledge distillation (KD) method based on reinforcement learning with a Large Language Model-induced Reward. Instead of directly training from LLM’s output, the process involves inducing a q-value function from the LLM’s policy (predicted probabilities) based on a widely adopted assumption. This is followed by further inducing a reward function based on the Bellman optimality equation. The induced reward function is subsequently used to distill LLM’s knowledge into the student, achieved by sampling a candidate sequence from the student-predicted distributions and evaluating it with the LLM-induced reward for policy gradient learning. This approach allows the student model to explore on its own during KD in a reinforcement learning (RL) fashion, thus alleviating the exposure bias problem.

Inducing Reward from LLMs

The reward induction starts by querying an LLM in a step-by-step fashion to obtain the next word probability pLLM(yty<t, pmtT(x)). This next-word probability is then used to induce a q-value function for step t, based on the assumption that an action is taken stochastically according to a Boltzmann distribution induced by q-values. The final step of reward induction is based on Bellman optimality, deriving an optimal q-value function from a reward. This leads to the derived reward RLLM(yt; y<t) which evaluates the appropriateness of a word yt at every step given its context y<t). This reward function is dense as opposed to various other heuristic rewards that only come at the end of a sequence.

Reinforcement Learning-Based KD

The derived reward function allows for reinforcement learning (RL) for KD, where a sequence is sampled from the student’s prediction, given by y ∼ qθ. Each word yt in y is then evaluated by the induced reward function (3), and the total reward of the sequence is RLLM(y) = X t RLLM(yt; y<t). Since the parameter θ occurs during the sampling process, RL is required to train θ in a trial-and-error manner because the gradient cannot be obtained by backpropagation. This differs from traditional sequence-level KD where the teacher teaches unilaterally with its own prediction, i.e., y ∼ pLLM.

Experimental Results and Analysis

Experiments were conducted on two text generation tasks: dialogue generation and text summarization, using datasets like DailyDialog, OpenSubtitles, and CNN/DailyMail. The results show that our LLMR approach largely outperforms traditional KD based on cross-entropy loss. Specifically, Table 1 shows that Our LLMR achieves superior performance across all the metrics and datasets compared to SeqKD, KL, RKL, and JS methods. Furthermore, the Diversity Analysis verifies that our LLMR yields much higher distinct scores than KL loss. Finally, the Exposure Bias Analysis confirms that our RL-based KD achieves low ExError% throughout different lengths, whereas KL- and RKL-based KD methods yield high exposure bias.

Conclusion

In this paper, we propose a novel knowledge distillation method, called LLMR, based on a large language model-induced reward function. Experiments on dialogue generation and text summarization show that our approach outperforms previous KD methods in terms of various metrics. We also conducted a detailed analysis to verify that our reinforcement learning-based method indeed alleviates the exposure bias problem present in common KD approaches.

Acknowledgments

We thank all reviewers and chairs for their valuable comments. The research is supported in part by the Natural Sciences and Engineering Research Council of Canada (NSERC) under Grant No. RGPIN2020-04465, an Alberta Innovates Project, the Amii Fellow Program, the Canada CIFAR AI Chair Program, a UAHJIC project, a donation from DeepMind, and the Digital Research Alliance of Canada (alliancecan.ca).

Bibliographical References

Toufique Ahmed and Premkumar Devanbu. 2022. Few-shot training LLMs for project-specific codesummarization. Kushal Arora, Layla El Asri, Hareesh Bahuleyan, and Jackie Cheung. 2022. Why exposure bias matters: An imitation learning perspective of error accumulation in language generation. Dzmitry Bahdanau, Philemon Brakel, Kelvin Xu, Anirudh Goyal, Ryan Lowe, Joelle Pineau, Aaron C. Courville, and Yoshua Bengio. 2017. An actor-critic algorithm for sequence prediction. Tom Brown et al. 2020. Language models are few-shot learners. Yu Cao et al. 2020. Pretrained language models for dialogue generation with multiple input sources. Alex J Chan and Mihaela van der Schaar. 2021. Scalable Bayesian inverse reinforcement learning. Ting-Rui Chiang and Yun-Nung Chen. 2021. Relating neural text degeneration to exposure bias. Thomas Degris, Martha White, and Richard S Sutton. 2012. Off-policy actor-critic. Yang Deng et al. 2023. Prompting and evaluating large language models for proactive dialogues: Clarification, target-guided, and non-collaboration. Yuxian Gu et al. 2024. MiniLLM: Knowledge distillation of large language models. Yongchang Hao et al. 2022. Teacher forcing recovers reward functions for text generation. Karl Moritz Hermann et al. 2015. Teaching machines to read and comprehend. Geoffrey Hinton et al. 2015. Distilling the knowledge in a neural network. Cheng-Yu Hsieh et al. 2023. Distilling step-by-step! outperforming larger language models with less training data and smaller model sizes. Yoon Kim and Alexander M Rush.

Improvements for AI systems

Bold header: Reinforcement Learning-Based Knowledge Distillation

This method allows the student model to explore on its own during KD in a reinforcement learning (RL) fashion, thus alleviating the exposure bias problem. This means the student model is not solely reliant on the teacher's previous predictions, enabling it to sample from its own distribution and receive a dense reward function that "evaluates the appropriateness of a word yt at every step given its context y<t."

Bold header: Superior Performance in Text Generation

The proposed LLMR approach largely outperforms traditional KD based on cross-entropy loss and achieves superior performance across all the metrics and datasets, specifically showing higher scores in Table 1 for dialogue generation (e.g., ROUGE-L score of 24.83 compared to SeqKD's 23.68).

Bold header: Enhanced Output Diversity

The RL mechanism allows the model to explore different regions of the sentence space, leading to much more diverse output, as evidenced by Table 2 where our LLMR yields much higher distinct scores compared to KL loss, which is noted for making models generate dull and short utterances.

Bold header: Exposure Bias Mitigation

The system demonstrably alleviates exposure bias because the analysis shows that our approach achieves low ExError% throughout different lengths, whereas methods like KL- and RKL-based KD yield high exposure bias, confirming that our approach alleviates exposure bias.