Direct Preference Density Alignment for Conversational Audio Equalization
- URL: http://arxiv.org/abs/2609.12607v1
- Date: Fri, 11 Sep 2026 09:05:08 GMT
- Title: Direct Preference Density Alignment for Conversational Audio Equalization
- Abstract summary: Direct Preference Density Alignment: An alternative framework that removes the need for a learned proxy reward model.<n>We leverage large-scale user data (approximately 90,000 samples) to construct non-parametric preference density maps.<n>In a blind audio equalization listening test, we show that this GRPO+DPO combination achieves the highest performance.
- Score: 12.358415132192148
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Large Language Model alignment typically relies on learned proxy reward models, which significantly increase the memory footprint during training and are notoriously prone to instability and reward hacking. While offline methods like Direct Preference Optimization (DPO) bypass the reward model, they lose the ability to perform online exploration. If no optimization constraints are applied, this can lead to format collapse in bounded, continuous spaces. To resolve this, we propose Direct Preference Density Alignment: An alternative framework that removes the need for a learned proxy reward model while strictly preserving the benefits of online reinforcement learning. We leverage large-scale user data (approximately 90,000 samples) to construct non-parametric preference density maps, establishing an empirical reward surface. In addition to removing the reward model, Direct Preference Density Alignment enables the combination of the online structural grounding of Group Relative Policy Optimization (GRPO) with the targeted offline refinement of DPO. We show that this GRPO+DPO combination achieves the highest performance, and in a blind audio equalization listening test, enables a 1.5B-parameter model to achieve perceptual parity with a carefully prompt-engineered GPT-4o mini baseline, using only a fraction of the inference compute.
Related papers
- FedPDPO: Federated Personalized Direct Preference Optimization for Large Language Model Alignment [55.97027207627]
Fed PDPO (Federated Personalized Direct Preference Optimization) is a personalized framework for preference alignment of large language models (LLMs)<n>It adopts a parameter-efficient fine-tuning architecture where each client maintains a frozen pretrained LLM backbone augmented with a Low-Rank Adaptation (LoRA) adapter, enabling communication-efficient aggregation.<n>Experiments on multiple preference datasets demonstrate state-of-the-art performance, achieving up to 4.80% average accuracy improvements in federated intra-domain and cross-domain settings.
arXiv Detail & Related papers (2026-03-20T08:24:49Z) - Tangent Space Fine-Tuning for Directional Preference Alignment in Large Language Models [0.2538209532048867]
Our goal is to enable large language models to balance human preference dimensions through principled and controllable alignment.<n>We propose Tangent-Space Direct Preference Optimization (TS-DPO), which performs DPO within a locally linear regime to learn per-objective update directions.
arXiv Detail & Related papers (2026-02-01T09:56:28Z) - Multi-dimensional Preference Alignment by Conditioning Reward Itself [32.33870784484853]
Multi Reward Conditional DPO resolves reward conflicts by introducing a disentangled Bradley-Terry objective.<n>Experiments on Stable 1.5 and SDXL demonstrate that MCDPO achieves superior performance on benchmarks.
arXiv Detail & Related papers (2025-12-11T02:44:31Z) - A Principled Loss Function for Direct Language Model Alignment [0.0]
We propose a novel loss function derived directly from the RLHF optimality condition.<n>Our proposed loss targets a specific finite value for the logits, which is dictated by the underlying reward, rather than its difference.<n>This inherent stability prevents reward hacking and leads to more effective alignment.
arXiv Detail & Related papers (2025-08-10T01:56:58Z) - Iterative Length-Regularized Direct Preference Optimization: A Case Study on Improving 7B Language Models to GPT-4 Level [50.897438358317686]
We show that iLR-DPO can enhance a 7B model to perform on par with GPT-4 without increasing verbosity.
Specifically, our 7B model achieves a $50.5%$ length-controlled win rate against $texttGPT-4 Preview$ on AlpacaEval 2.0.
arXiv Detail & Related papers (2024-06-17T17:55:38Z) - Self-Augmented Preference Optimization: Off-Policy Paradigms for Language Model Alignment [104.18002641195442]
We introduce Self-Augmented Preference Optimization (SAPO), an effective and scalable training paradigm that does not require existing paired data.
Building on the self-play concept, which autonomously generates negative responses, we further incorporate an off-policy learning pipeline to enhance data exploration and exploitation.
arXiv Detail & Related papers (2024-05-31T14:21:04Z) - Value-Incentivized Preference Optimization: A Unified Approach to Online and Offline RLHF [80.32171988565999]
We introduce a unified approach to online and offline RLHF -- value-incentivized preference optimization (VPO)<n>VPO regularizes the maximum-likelihood estimate of the reward function with the corresponding value function.<n>Experiments on text summarization and dialog verify the practicality and effectiveness of VPO.
arXiv Detail & Related papers (2024-05-29T17:51:42Z) - Robust Preference Optimization through Reward Model Distillation [68.65844394615702]
Direct Preference Optimization (DPO) is a popular offline alignment method that trains a policy directly on preference data.<n>We analyze this phenomenon and use distillation to get a better proxy for the true preference distribution over generation pairs.<n>Our results show that distilling from such a family of reward models leads to improved robustness to distribution shift in preference annotations.
arXiv Detail & Related papers (2024-05-29T17:39:48Z) - Self-Play Preference Optimization for Language Model Alignment [75.83359213697854]
Recent advancements suggest that directly working with preference probabilities can yield a more accurate reflection of human preferences.
We propose a self-play-based method for language model alignment, which treats the problem as a constant-sum two-player game.
Our approach, dubbed Self-Play Preference Optimization (SPPO), utilizes iterative policy updates to provably approximate the Nash equilibrium.
arXiv Detail & Related papers (2024-05-01T17:59:20Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.