# RLHF Fine-Tuning of LLMs for Alignment with Implicit User Feedback in Conversational Recommenders

arXiv: https://arxiv.org/abs/2508.05289 · HTML: https://mlsanity.com/paper/2508.05289/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Placed in Preference alignment for recommenders and RAG, one of 10 directions in LLM alignment / RLHF / preference optimisation.
- Builds on 20 of its 34 references that are in the map.
- First posted on arXiv 7 Aug 2025.

## Placed in

- LLM alignment / RLHF / preference optimisation / [Preference alignment for recommenders and RAG](https://mlsanity.com/alignment/preference-alignment-for-recommenders-and-rag/)

## Builds on (most cited first, 20 of 20)

- [RLHF Deciphered: A Critical Analysis of Reinforcement Learning from Human Feedback for LLMs](https://mlsanity.com/paper/2404.08555/) (2024)
- [Enhancing Code LLMs with Reinforcement Learning in Code Generation: A Survey](https://mlsanity.com/paper/2412.20367/) (2024)
- [Capabilities of Large Language Models in Control Engineering: A Benchmark Study on GPT-4, Claude 3 Opus, and Gemini 1.0 Ultra](https://mlsanity.com/paper/2404.03647/) (2024)
- [A Multimodal Fusion Network For Student Emotion Recognition Based on Transformer and Tensor Product](https://mlsanity.com/paper/2403.08511/) (2024)
- [Research on Splicing Image Detection Algorithms Based on Natural Image Statistical Characteristics](https://mlsanity.com/paper/2404.16296/) (2024)
- [RobustKV: Defending Large Language Models against Jailbreak Attacks via KV Eviction](https://mlsanity.com/paper/2410.19937/) (2024)
- [The Role of Deductive and Inductive Reasoning in Large Language Models](https://mlsanity.com/paper/2410.02892/) (2024)
- [Bayesian Optimization for Controlled Image Editing via LLMs](https://mlsanity.com/paper/2502.18116/) (2025)
- [Enhanced Recommendation Combining Collaborative Filtering and Large Language Models](https://mlsanity.com/paper/2412.18713/) (2024)
- [Research on the Design of a Short Video Recommendation System Based on Multimodal Information and Differential Privacy](https://mlsanity.com/paper/2504.08751/) (2025)
- [Review-based Recommender Systems: A Survey of Approaches, Challenges and Future Perspectives](https://mlsanity.com/paper/2405.05562/) (2024)
- [User Behavior Analysis in Privacy Protection with Large Language Models: A Study on Privacy Preferences with Limited Data](https://mlsanity.com/paper/2505.06305/) (2025)
- [Machine Learning-Based Prediction of Metal-Organic Framework Materials: A Comparative Analysis of Multiple Models](https://mlsanity.com/paper/2507.04493/) (2025)
- [LLM-Driven E-Commerce Marketing Content Optimization: Balancing Creativity and Conversion](https://mlsanity.com/paper/2505.23809/) (2025)
- [Machine Learning based Enterprise Financial Audit Framework and High Risk Identification](https://mlsanity.com/paper/2507.06266/) (2025)
- [Research on Personalized Financial Product Recommendation by Integrating Large Language Models and Graph Neural Networks](https://mlsanity.com/paper/2506.05873/) (2025)
- [Research on E-Commerce Long-Tail Product Recommendation Mechanism Based on Large-Scale Language Models](https://mlsanity.com/paper/2506.06336/) (2025)
- [A Deep Learning Algorithm Based on CNN-LSTM Framework for Predicting Cancer Drug Sales Volume](https://mlsanity.com/paper/2506.21927/) (2025)
- [Meta-Learning for Cold-Start Personalization in Prompt-Tuned LLMs](https://mlsanity.com/paper/2507.16672/) (2025)
- [Extended Self-Critical Pipeline for Transforming Videos to Text (TRECVID-VTT Task 2021) -- Team: MMCUniAugsburg](https://mlsanity.com/paper/2112.14100/) (2021)

## Built on by (most cited first, 5 of 5)

- [Efficient Cold-Start Recommendation via BPE Token-Level Embedding Initialization with LLM](https://mlsanity.com/paper/2509.13179/) (2025)
- [Instructional Prompt Optimization for Few-Shot LLM-Based Recommendations on Cold-Start Users](https://mlsanity.com/paper/2509.09066/) (2025)
- [IceBreaker for Conversational Agents: Breaking the First-Message Barrier with Personalized Starters](https://mlsanity.com/paper/2604.18375/) (2026)
- [Adaptive Data Augmentation with Multi-armed Bandit: Sample-Efficient Embedding Calibration for Implicit Pattern Recognition](https://mlsanity.com/paper/2602.19385/) (2026)
- [Your Mouse and Eyes Secretly Leak Your Preference: LLM Alignment using Implicit Feedback from Users](https://mlsanity.com/paper/2606.20482/) (2026)

## Abstract (arXiv)

Conversational recommender systems (CRS) based on Large Language Models (LLMs) need to constantly be aligned to the user preferences to provide satisfying and context-relevant item recommendations. The traditional supervised fine-tuning cannot capture the implicit feedback signal, e.g., dwell time, sentiment polarity, or engagement patterns. In this paper, we share a fine-tuning solution using human feedback reinforcement learning (RLHF) to maximize implied user feedback (IUF) in a multi-turn recommendation context. We specify a reward model Rφ learnt on weakly-labelled engagement information and maximize user-centric utility by optimizing the foundational LLM Mθ through a proximal policy optimization (PPO) approach. The architecture models conversational state transitions st → at → st +1, where the action at is associated with LLM-generated item suggestions only on condition of conversation history in the past. The evaluation across synthetic and real-world datasets (e.g.REDIAL, OpenDialKG) demonstrates that our RLHF-fine-tuned models can perform better in terms of top-k recommendation accuracy, coherence, and user satisfaction compared to (arrow-zero-cmwrquca-teja-falset ensuite 2Round group-deca States penalty give up This paper shows that implicit signal alignment can be efficient in achieving scalable and user-adaptive design of CRS.