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LIRE: listwise reward enhancement for preference alignment
Computation and Language · Submitted 22 May 2024
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim Our experiments demonstrate that LIRE consistently outperforms existing methods across several benchmarks on dialogue and summarization tasks, with good transferability to out-of-distribution data, assessed using proxy reward models and human annotators.
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The abstract, sentence by sentence
Recently, tremendous strides have been made to align the generation of Large Language Models (LLMs) with human values to mitigate toxic or unhelpful content. Leveraging Reinforcement Learning from Human Feedback (RLHF) proves effective and is widely adopted by researchers. However, implementing RLHF is complex, and its sensitivity to hyperparameters renders achieving stable performance and scalability challenging. Furthermore, prevailing approaches to preference alignment primarily concentrate on pairwise comparisons, with limited exploration into multi-response scenarios, thereby overlooking the potential richness within the candidate pool. For the above reasons, we propose a new approach: Listwise Reward Enhancement for Preference Alignment (LIRE), a gradient-based reward optimization approach that incorporates the offline rewards of multiple responses into a streamlined listwise framework, thus eliminating the need for online sampling during training. LIRE is straightforward to implement, requiring minimal parameter tuning, and seamlessly aligns with the pairwise paradigm while naturally extending to multi-response scenarios. Moreover, we introduce a self-enhancement algorithm aimed at iteratively refining the reward during training. Our experiments demonstrate that LIRE consistently outperforms existing methods across several benchmarks on dialogue and summarization tasks, with good transferability to out-of-distribution data, assessed using proxy reward models and human annotators.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | Recently, tremendous strides have been made to align the generation of Large Language Models (LLMs) with human values to mitigate toxic or unhelpful content. | background 1.00 | 0.03 | 0.01 | asserted 1.00 |
| s1 | Leveraging Reinforcement Learning from Human Feedback (RLHF) proves effective and is widely adopted by researchers. | background 1.00 | 0.04 | 0.02 | asserted 1.00 |
| s2 | However, implementing RLHF is complex, and its sensitivity to hyperparameters renders achieving stable performance and scalability challenging. | background 0.55 | 0.07 | 0.02 | asserted 1.00 |
| s3 | Furthermore, prevailing approaches to preference alignment primarily concentrate on pairwise comparisons, with limited exploration into multi-response scenarios, thereby overlooking the potential richness within the candidate pool. | gap 0.99 | 0.05 | 0.02 | asserted 0.99 |
| s4 | For the above reasons, we propose a new approach: Listwise Reward Enhancement for Preference Alignment (LIRE), a gradient-based reward optimization approach that incorporates the offline rewards of multiple responses into a streamlined listwise framework, thus eliminating the need for online sampling during training. | contribution 0.74 | 0.81 | 0.17 | asserted 0.99 |
| s5 | LIRE is straightforward to implement, requiring minimal parameter tuning, and seamlessly aligns with the pairwise paradigm while naturally extending to multi-response scenarios. | contribution 0.78 | 0.55 | 0.09 | asserted 1.00 |
| s6 | Moreover, we introduce a self-enhancement algorithm aimed at iteratively refining the reward during training. | method 0.53 | 0.90 | 0.04 | asserted 0.99 |
| s7 | Our experiments demonstrate that LIRE consistently outperforms existing methods across several benchmarks on dialogue and summarization tasks, with good transferability to out-of-distribution data, assessed using proxy reward models and human annotators. | result 1.00 | 0.96 | 0.88 | asserted 1.00 |
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The paper
Original abstract
Recently, tremendous strides have been made to align the generation of Large Language Models (LLMs) with human values to mitigate toxic or unhelpful content. Leveraging Reinforcement Learning from Human Feedback (RLHF) proves effective and is widely adopted by researchers. However, implementing RLHF is complex, and its sensitivity to hyperparameters renders achieving stable performance and scalability challenging. Furthermore, prevailing approaches to preference alignment primarily concentrate on pairwise comparisons, with limited exploration into multi-response scenarios, thereby overlooking the potential richness within the candidate pool. For the above reasons, we propose a new approach: Listwise Reward Enhancement for Preference Alignment (LIRE), a gradient-based reward optimization approach that incorporates the offline rewards of multiple responses into a streamlined listwise framework, thus eliminating the need for online sampling during training. LIRE is straightforward to implement, requiring minimal parameter tuning, and seamlessly aligns with the pairwise paradigm while naturally extending to multi-response scenarios. Moreover, we introduce a self-enhancement algorithm aimed at iteratively refining the reward during training. Our experiments demonstrate that LIRE consistently outperforms existing methods across several benchmarks on dialogue and summarization tasks, with good transferability to out-of-distribution data, assessed using proxy reward models and human annotators.
Human review
How the literature uses it
4 arXiv papers cite this in 4 sentences
Papers citing it · 2 sentences without a confident label
Cites it as background 2 papers
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Among the wide range of algorithms proposed for preference alignment [ref] , the most prominent are perhaps RLHF and DPO.
On-the-fly Preference Alignment via Principle-Guided Decoding · traditional preference alignment · 1.00
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There are various alignment methods proposed recently, such as RLHF, DPO, RRHF [ref] , LIRE [ref] , CPO [ref] and KTO [ref] .
FlipGuard: Defending Preference Alignment against Update Regression with Constrained Optimization · Regression in Alignment · 1.00
What it cites · 27 sentences without a confident label
Uses the method or tool of 3 papers
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To investigate how the well-trained models with the dialogue dataset perform on other out-of-distribution conversation cases, we leverage MT-Bench introduced in [ref] , which contains 80 open-ended questions for evaluating chat assistants.
Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena · Performance comparison when training with pairwise preference · 0.84
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For the dialogue task, we follow [ref] to sample responses from Alpaca-7B [ref] using diverse beam search and extend the sequence length from the original human preference pair to 4 and 6, respectively.
RRHF: Rank Responses to Align Language Models with Human Feedback without tears · Effects of increasing sequence number · 0.92
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First we explore with Statistical Rejection Sampling Optimization (RSO) [ref] , which is a special technique to source preference data from the estimated target optimal policy using rejection sampling, and we think it intriguing to combine RSO when constructing the candidate pool.
Statistical Rejection Sampling Improves Preference Optimization · Additional performance boost with special sampling technique and iterative training · 0.95
Cites as background 16 papers
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Existing methods focus on employing Reinforcement Learning from Human Feedback (RLHF) to fine-tune the pre-trained LLMs [ref] , which introduces a paradigm that involves leveraging supervised fine-tuning (SFT) on the initial models, fitting the reward model to human preferences, and then using Reinforcement Learning (RL) algorithms such as Proximal Policy Optimization (PPO) [ref] to optimize a policy that doesn't drift overly far from the original model.
Reinforcement Learning from Diverse Human Preferences · Introduction · 0.93
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Existing methods focus on employing Reinforcement Learning from Human Feedback (RLHF) to fine-tune the pre-trained LLMs [ref] , which introduces a paradigm that involves leveraging supervised fine-tuning (SFT) on the initial models, fitting the reward model to human preferences, and then using Reinforcement Learning (RL) algorithms such as Proximal Policy Optimization (PPO) [ref] to optimize a policy that doesn't drift overly far from the original model.
Proximal Policy Optimization Algorithms · Introduction · 0.93
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Alternatively, methods such as DPO [ref] and RRHF [ref] primarily leverage a pairwise comparison between a positive and a negative sample and transform the RL problem into an offline setting.
RRHF: Rank Responses to Align Language Models with Human Feedback without tears · Introduction · 0.83
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