TangoFlux: Super Fast and Faithful Text to Audio Generation with Flow Matching and Clap-Ranked Preference Optimization
Authors: Chia-Yu Hung
Navonil Majumder
Zhifeng Kong
Ambuj Mehrish
Amir Zadeh
Chuan Li
Rafael Valle
Bryan Catanzaro
Soujanya Poria
Venue: ICLR 2026 Poster (2026)
Keywords: text to audio
flow matching
preference optimization
Primary area: applications to computer vision, audio, language, and other modalities
TL;DR: Text to audio generation with semi online preference optimization performed on self-generated audio samples

Abstract: We introduce TangoFlux, an efficient Text-to-Audio (TTA) generative model with 515M parameters, capable of generating up to 30 seconds of 44.1kHz audio in 3.7 seconds on a A40 GPU. A key challenge in aligning TTA models lies in creating preference pairs, as TTA lacks structured mechanisms like verifiable rewards or gold-standard answers available for Large Language Models (LLMs). To address this, we propose CLAP-Ranked Preference Optimization (CRPO), a novel framework that iteratively generates and optimizes preference data to enhance TTA alignment. We show that the audio preference dataset generated using CRPO outperforms the static alternatives. With this framework, TangoFlux achieves state-of-the-art performance across both objective and subjective benchmarks. https://tangoflux.github.io/ holds the model-generated audio samples for comparison.

Published as a conference paper at ICLR 2026
TanGOFlux: SUPER FAST AND FAITHFUL TEXT TO AU- DIO GENERATION WITH FLOW MATCHING AND CLAP- RANKED PREFERENCE OPTIMIZATION
Chia-Yu Hung' Navonil Majumder
Zhifeng Kong²
Ambuj Mchrish Bryan Catanzaro² Soujanya Poria'
Amir Ali Bagherzadeh' Chuan Li
Rafael Valle²
Nanyang Technology University (NTU) NVIDIA
"Ca' Foscari University of Venice
'Lambda Labs
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{navonil ma junder, sou wow.jjjaa.....aaattti...
(zkong, rafaelvalle, boatanzaro))eniddi.cco famirali.zades c}@lambdal.com com
ABSTRACT
We introduce TANGOFLUX, efficient Text-to-Audio (TTA) generative model with 515M parameters, capable of generating up to 30 seconds of 44. 1kHz audio in 3.7 seconds A40 GPU. A key challenge in aligning TTA models lies in creating preference pairs, as TTA lacks structured mechanisms like verifiable re- wards gold-standard answers available for Large Language Models (LLMs). To address this, we propose CLAP-Ranked Preference Optimization (CRPO), novel framework that iteratively generates and optimizes preference data to enhance TTA alignment. We show that the audio preference dataset generated using CRPO outperforms the static alternatives. With this framework, TANGOFLUX achieves state-of- the-art performance across both objective and subjective benchmarks. https: : /tt::///aa /ttannoofluu git.ub..h...g io/ holds the model-generated audio sam- ples for comparison.
INTRODUCTION
Audio is integral to daily life and creative industries, from enhancing communication and storytelling to enriching experiences in music, sound effects, and podcasts. However, creating high-quality audio, such as foley effects or music compositions, demands significant effort, expertise, and time. Recent advancements in text-to-audio (TTA) generation (Majumder al., 2024; Ghosal al., 2023; Liu et al., 2023; 2024b; Xue et al., 2024; Vyas et al., 2023; Huang et al., 2023b;a) enabled automatic and rapid creation of diverse and expressive audio content directly from textual descriptions. This technology holds immense potential to accelerate audio production and creative multimedia workflows. However, many existing models face challenges with controllability, often struggling fully capture the details in the input prompts, especially when the prompts are complex, containing many events with diverse temporal relationships. This sometimes results in audios that omit certain events or place the events in wrong order. At times, the generated audio may even contain input-adjacent, but unmentioned and unintended, events, that could be characterized as hallucinations.
Alignment often leverages reinforcement learning from human feedback (RLHF) or other reward- based optimization methods to endow the generated outputs with human preferences, ethical con- siderations, and task-specific requirements (Ouyang et al., 2022). Recently Majumder et al. (2024) employed alignment for TTA model training. One critical challenge in implementing alignment for TTA lies in the creation of preference pairs. Unlike LLM alignment, where off the-shelf reward models (Lambert et al., 2024a;b) and human feedback data or verifiable gold answers are available, TTA domain as yet lacks such tooling.

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While audio language models (Chu al., 2024; 2023; Tang et al., 2024) can take audio inputs and generate textual outputs, they usually produce noisy feedback, unfit for preference pair creation for audio. BATON (Liao et al., 2024) employs human annotators to assign binary label 0/1 to each audio sample based on its alignment with a given prompt. However, such labor-intensive manual approach is often impractical at large scale.
To address these issues, we propose CLAP-Ranked Preference Optimization (CRPO), a simple yet effective approach generate audio preference data and perform preference optimization on rectified flows. As shown in Fig. 1, CRPO consists of iterative cycles of audio sampling, preference pair curation, and preference optimization, resembling a ieproveppee algorithm. A notable aspect of this approach is evolution by generating its own training dataset, dynamically aligning itself over multiple iterations. We show the CLAP (Wu* et al., 2023) can serve as proxy reward model for ranking generated audios by alignment with the text description. With this ranking, we construct an audio preference dataset that post alignment yields superior performance to other static audio preference datasets, such as, BATON and Audio-Alpaca (Majumder et al., 2024).
Unlike many closed TTA models (Evans et al., 2024b;a; Copet et al., 2024) that are trained on proprietary data, our open-source TANGOFLUX is trained open data, achieving slate-of-the- art performance on benchmarks and TANGOFLUX also supports variable-duratin audio generation up to 30 seconds with an inference time of 3.7 seconds on an A40 GPU. This is unlike diffusion--ased TTA models (Ghosal et al., 2023; Majumder et al., 2024; Liu et al., 2024b) that known to require too many denoising steps for decent output, consuming much compute and time. This is achieved using a transformer (Vaswani al.. 2023) backbone that undergoes pretraining, fine-tuning, and preference optimization with rectified flow matching training objective.
human evaluation, despite its smaller size.
Our contributions:
(i) We introduce T TANGOFLUX, small TTA model based on rectified flow with otate-of-the-art performance for fully non-proprietary training data.
(ii) We propose CRPO, a simple yet effective strategy for dynamically generating audio pref- erence data and aligning rectified flows. By iteratively refining the preference data, CRPO continuously improves itself, outperforming static audio preference datasets.
(iii) We conduct extensive experiments and highlight the importance of each component of CRPO in aligning rectified flows for improving scores on benchmarks.
(iv) We will release the code and model weights.
METHOD
TANGOFLUX consists of FluxTransformer blocks, which are composed of Diffusion Transformer (DiT) (Peebles & Xie, 2023) and Multimodal Diffusion Transformer (MMDiT) (Esser et al., 2024) conditioned on text prompt and duration embedding to generate 1kHz audios of up to second long. OANGOFLUX learns rectified flow trajectory to the latent audio representation encoded by variational autoencoder (VAE) (Kingma & Welling, 2022). As shown Fig. 1, the training pipeline consists of two stages: pre-training and alignment. TaNGOFLUX is aligned with our CRPO method which iteratively generates new synthetic data and constructs preference pairs for preference optimization.
2.1 AUDIO ENCODING
We use the VAE from Stable Audio Open (Evans et al., 2024c), which is capable of encoding 44. kKZ stereo audio waveforms into latent representations. Given a stereo audio X E 2x with d as the duration and sr as the sampling rate, the VAE encodes X into a latent representation Z E R c with L and C' being the latent sequence length and channel size, respectively. The VAE decodes the latent representation Z into the original stereo audio X. The entire VAE is kept frozen during TANGo FLUX training.
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Stabale VAF
Blc LaT
Sampling
Training
Dataset
WavCaps
Hompr
Aud
AudioCops
AudioCaps
H++
TANGOFLUX
langohlux base Ag
Nusto
Oeoertt
# CLAP
Aude
1. Pre-training
2. Online iterative Alignment
Figure depiction of the overall training pipeline of TANGOFLUX.
2.2 MODEL CONDITIONING
To control the generation of audio of varying lengths, we employ (i) text conditioning to control the content of the generated audio and (ii) duration conditioning dictate the output audio length, up to maximum of 30 seconds.
Text Conditioning, We obtain an encoding Ctext of the given textual description from pretrained text-encoder. Given the strong performance of FLAN-T5 (Chung et al., 2022; Raffel et al., 2023) as conditioning text-to-audio generation (Majumder et al., 2024; Ghosal et al., 2023), we select FLAN-TS as our text encoder.
Duration Encoding. Inspired by the recent works (Evans et al., 2024c;a:b), to generate audios with variable length, use small neural network to encode the audio duration into duration embedding Cdur that concatenated with the text encoding Stext and fed into TANGOFLUX to control the fixed-length latent space
duration of audio output. Crucially, TANGOFLUX always operates (30s audio). The duration conditioning Cdur explicitly controls how much this fixed-length latent space contains actual audio content, and how much is dedicated silence.
2.3 MODEL ARCHITECTURE
Following the recent success of FLUX models in image generation's we adopt hybrid MMDiT and DiT architecture as the backbone for T TANGOFLUX. While MMDiT blocks demonstrated a strong performance, simplifying some of them into single DiT block improved scalability and parameter efficiency². These lead us to select a model architecture with blocks of MMDiT, followed by 18 blocks of DiT. Each block has 8 attention heads of 128 width, totaling a width of 1024. This setting amounts to 51SM trained parameters.
2.4
FLOW MATCHING
Several generative models have been successfully trained under the diffusion framework (Ho et al., 2020; Song et al., 2022; Liu et al., 2022). However, this approach is known to be sensitive to the choice of noise scheduler, which may significantly affect performance. In contrast, the flow matching (FM) framework (Lipman et al., 2023; Albergo & Vanden-Eijnden, 2023) has been shown to be more robust to choice of noise scheduler, making a preferred choice in many applications, including text-to-audio (TTA) and text-to-speech (TTS) tasks (Liu et al., 2024a; Le et al., 2023; Vyas et al., 2023).
Flow matching builds upon the continuous normalizing flows framework (Onken al., 2021). It generates samples from target distribution by learning time-dependent vector field that maps
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samples from simple prior distribution (e.g., Gaussian) complex target distribution. Prior work in TTA, such as AudioBox (Vyas et al., 2023) and Voicebox (Le et al., 2023), has predominantly adopted the Optimal Transport conditional path proposed by (Lipman al., 2023). However, utilize rectified flows (Liu et al., 2022) instead, which is straight line path from noise to distribution, corresponding to the shortest path. See Appendix A.4 for the details rectified flow.
Inference. For inference, we sample noise To ~ Oo, and use Euler solver to compute 21+ based on the model-predicted velocity u(:; 0) at each time step 1. The results for the Heun solver presented in Appendix A.7.
2.5 CLAP-RANKED PREFERENCE OPTIMIZATION
CLAP-Ranked Preference Optimization (CRPO) leverages text-audio joint-embed embedding model like CLAP (Wu* et al., 2023) as proxy reward model to rank the generated audios by similarity with the input description and subsequently construct the preference pairs.
Tango2 (Majumder et al., 2024) and CLIP-DPO (Ouali et al., 2024) curate preference pairs via prompt perturbation, sampling multiple outputs for each prompt to establish static rankings. However, these approaches rely static preference datasets, which can limit alignment generalization and adaptability. contrast, we adopt a dynamic preference optimization strategy: it generates new synthetic preference pairs at the start of each training iteration. This online data generation allows the model to continually refine its alignment through training which we show to further improve performance.
We set Ti to a pre-trained checkpoint TANGOFLuX-base to align. Thereafter, CRPO iteratively aligns checkpoint Ty ul: 0g) into checkpoint 7k+1, starting from k = 0. Each alignment iteration consists of three steps: (i) batched online data generation, (ii) reward estimation and preference dataset creation, and (iii) fine-tuning ne into T via direct preference optimization. This alignment process allows the model to continuously self-improve by generating and leveraging its own preference data.
This approach of alignment is inspired by few LLM alignment approaches (Zelikman al., 2022; Kim et al.. 2024a; Yuan et al., 2024; Pang et al., 2024). However, there are key distinctions to our work: (i) we align rectified flows for audio generation, rather than autoregressive language models; (ii) while LLM alignment benefits from numerous off-the-shelf reward models (Lambert et al., 2024b). which ease the construction of preference datasets based on reward scores, L.MM judged outputs, or programmatically verifiable answers, the audio domain lacks such models or method for evaluating audio. We demonstrate that the CLAP model can serve as an effective proxy audio reward model, enabling the creation of preference datasets (see Appendix A.3). Finally, we highlight the necessity of generating online data at every iteration, as iterative optimization on offline data leads to quicker performance saturation and subsequent degradation.
2.5.1 CLAP A REWARD MODEL.
CLAP reward score is calculated as the cosine similarity between textual and audio embeddings encoded by the model. Thus, we assume that CLAP can serve as a reasonable proxy reward model for evaluating audio outputs against the textual description. In Appendix A.3, we demonstrate that using CLAP as a judge to choose the best-of-N inferred policies improves performance terms of objective metrics.
2.5.2 BATCHED ONLINE DATA GENERATION
To construct preference dataset at iteration k. we first sample set of prompts Mg from a larger pool B. Then, we generate N audios for each prompt y; M2 using nu and use CLAP3 (Wu* et al., 2023) to rank those audios by similarity with ys- For each prompt Vi, we select the highest-rewarded or ranking audio x as the winner and the lowest-rewarded audio 2! as the loser, yielding preference dataset Dj-
[(x 2/. 3:) y: Mx).
ob/ma////aaaam i n/ /60k aud oset-best...
pt
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2.5.3 PREFERENCE OPTIMIZATION
Direct preference optimization (DPO) (Rafailov et al., 2024c) is shown to be effective at instilling human preferences in LLMs (Ouyang et al., 2022). Consequently, DPO is successfully translated into DPO-Diffusion (Wallace et al., 2023) for alignment of diffusion models. The DPO-diffusion loss defined as
DDPO.DiI -B,,, e" e log (-8 ca (=x2))1 || (rr(==;)1
ll - co(2 + llea Eree Erer(z)))B
(I)
(0, T) is diffusion step among T steps; z and प; represent the losing and winning audios, with ~ (0,1)
Following Esser al. (2024), DPO-Diffusion loss applicable to rectified flow through the equiv-
alence (Lipman et al., 2023) between and u)=;),, thereby the noise matching loss terms can be substituted with flow matching terms:
LDPO-PM = -1 1), at log of | v, 11'
| t; 0) vz 11
Winning loss
Lusing loss
-I[u(az" M, L; - 1 + | 1, 4;0,) 111 113) (2)
Winning relerence loss
Losing relerence loss.
where is flow matching timestep and x and x. represent losing and winning audio, respectively,
The DPO loss for L.Mss models the relative likelihood of the winner and loser responses, allowing minimization of the loss by increasing their margin, even both log-likelihoods decrease (Pal et al., 2024). As DPO optimizes the relative likelihood of the winning responses over the losing ones, not their absolute values, convergence actually requires both likelihoods to decrease despite being counterintuitive (Rafailov al., 2024b). The decrease in likelihood does not necessarily decrease performance, but required for improvement (Rafailov et al., 2024a). However, in the context of rectified flows, this behavior is less clear due to the challenges in estimating the likelihood of generating samples with classifier-free guidance (CFG). A closer look at LDPO-FM (Eq. (2)) reveals that it can similarly be minimized by increasing the margin between the winning and losing losses, even if both losses increase. In Section 4.5, we demonstrate that preference optimization of rectified flows via CDPO-FM suffer from this phenomenon as well.
To remedy this, we directly add the winning loss to the optimization objective to prevent winning loss from increasing:
LCRPO = LDPO.FM + LIM:
where LFM is the flow matching loss computed on the winning audio as shown in Eq. (5). While the DPO loss is effective improving preference rankings between chosen and rejected audio, relying on it alone can lead to overoptimization. This can distort the semantic and structural fidelity of the winning audio, causing the model's outputs drift from the desired distribution. Adding the Crm component mitigates this risk by anchoring the model to the high-quality attributes of the chosen data. This regularization stabilizes training and preserves the essential properties of the winning examples, ensuring a balanced and robust optimization process. Our empirical results demonstrates LerpO outperform LDPO-FM as shown Section 4.5.
EXPERIMENTS
3.1 MODEL TRAINING
We pretrained TANGOFLUX on Wavcaps (Mei et al., 2024) dataset for 80 epochs with the AdamW (Loshchilov & Hutter, 2019), 31 0.9, B2 0.95, max learning rate of 5 x 10-4. During the aligmment phase, we used the same optimizer, but an overall batch size of 48 and maximum learning rate of 10-5, See Appendix A.5 for the complete training details.
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3.2 DATASETS
Training dataset. We use complete open source data which consists of approximately 400k audios from Wavcaps (Mei et al., 2024) and 45k audios from the training set of AudioCaps. (Kim al., 2019). Audios shorter than 30 seconds are padded with silence to 30s. Longer than 30 second audios are center cropped to 30 seconds. As the audio files are mono, we duplicated the channel to create pseudostereo audios for compatibility with the VAE.
CRPO dataset. We initialize the prompt bank as the prompts of AudioCaps training set, with a total of 45k prompts. At the start of each iteration of CRPO, we randomly sample 20k prompts from the prompt bank and generate 5 audios per prompt, and use the CLAP model to construct 20k preference pairs.
Evaluation dataset. For the main results, we evaluated TANGOFLUX on the AudioCaps test set, using the same 886-sample split (Majumder et al., 2024). Objective metrics are reported this subset. Additionally, we categorized AudioCaps prompts using GPT-4 to identify those with multiple distinct events, such as "Birds chirping and thunder strikes," which includes "sound of birds chirping" and "sound of thunder." Subjective evaluation was conducted on an out-of-distribution dataset with 50 challenging prompts.
3.3 OBJECTIVE EVALUATION
Baselines, We compare TANGOFLUUX to few existing strong text-to-audio generation baselines: Tango. Tango 2, Audi OLDM Stable Audio Open, Audiok, ox, and GenAU-Full-I, in- cluding the previous SOTA models. Across the baselines, we use the default recommended classifier- free guidance (CFG) scale (Ho & Salimans, 2022) and number of steps. For TANGOFLUX, we use CFG scale of 4.5 and 50 steps for inference. Since TANGOFLUX and Stable Audio Open allow variable audio generation length, we set the duration conditioning to 10 seconds and use the first 10 seconds of generated audio to perform the evaluation. We also report the effect of CFG scale in the Appendix A.6.
Evaluation metrics. We evaluate TANGOFLUX using both objective and subjective metrics. Follow- ing (Evans et al., 2024a), we report the five objective metrics: Kernel Audio Distance (KAD) et al., 2025), Fréchet Distance (FDopenls) (Cramer et al., 2019), Kullback-Leibler divergence (KLpasst) CLAP and Inception Score (IS) (Salimans et al., 2016). These metrics allow high-quality audio evaluation up to 48kHz. Further details these metries presented in Appendix A.10.
3.4 HUMAN EVALUATION
Following prior studies (Ghosal et al., 2023; Majumder et al., 2024), our subjective evaluation covers two key attributes of the generated audio: overall audio quality (OVL) and relevance to the text input (REL). OVL captures the general sound quality, including clarity and naturalness, ignoring the alignment with the input prompt. In contrast, REL quantifies the alignment of the generated audio with the provided text input. At least four annotators rate each audio sample on a scale from (worst) to 100 (best) on both OVL and REL. This evaluation is performed on 50 GPT4o- --g-erereeddedd and human-vetted prompts and reported in terms of three metrics: z-score, Ranking, and Elo score. The evaluation instructions, annotators, and metrics are in Appendix A.12. Due to resource constraint, we were not able to peform human evaluation on all the models.
RESULTS
4.1
MAIN RESULTS
Table objectively compares TANGOFLUX with prior text-to-audio generation models on AudioCaps. Our results suggest that TANGOFLUX consistently outperforms the prior works all objective metrics, except Tanço on KL and FDp. We hypothesize this specific discrepancy is attributable to the different sampling rates used for the different FD metrics. FDp operates 16 kHz, hence it is required to downsample TANGOFLUR generated audio, removing all of its high-frequency details. Conversely, the metric rpppe evaluated directly the 48 kHz sampling rate being sensitive the

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Table Comparison of text-lo-audio models. Output length represents the duration of the generated
audio. Objective metrics include FD for Fréchet Distance measured with OpenL3, FDp for for
Fréchet Distance measured with PANNs, Kl pusa for KL divergence, and CLAP score score for alignment.
Inference Time (8)
Model
#Farams
Duration
Steps
FD,
PDopeet
KL
KADI
CLAP 1
IS1
Few step sampling models
consistency1TA
559M
10 sec
20.9
94.6
1.43
0.61
0.377
9.1
<0.2
AudieLeM
160M
10
19.2
107.4
1.58
0,56
0.363
10.2
<0,2
Andi CTTM 2-large
712M
10 sec
200
33.2
108.3
1.81
1.78
0.419
7.9
24.8
Make-An-Ancio
160M
100
15.6
98.7
1.33
0.45
0.406
9.4
2.3
EzAudio-Xi
874M
sec
200
15.8
84.7
1.20
0.15
0,460
10.8
12.2
Stable Andio Open
1056M
47
100
42.6
89.2
2.58
4.15
0.291
9.9
8,6
Tango
866M
sec
200
24.5
107.9
1.20
1.71
0.407
7.8
22.8
Tango
866M
10 sec
200
20.8
108.4
1.11
1,38
0.447
9.0
22.8
GenAU-Fulll-
1,258
0.447
Andiex
1.1B
10 sec
250
25.2
77.5
1.56
1.30
0,380
T0.0
9.6
FANGOFLUN-base
0.67
11.7
TANGOFLUX
316M
30 sec
50
20.3
75,1
1.15
0.60
0.480
12.2
3.7
higher frequency details, where TANGOFLUX shows advantage for being able directly generate audios at 44. Khz.
4.2 HUMAN EVALUATION RESULTS
The results of the human evaluation are presented in Table with detailed comparisons of the models across the evaluated metrics: z-scores, rankings, and Elo scores for both overall audio quality (OVL) and relevance to the text input (REL).
z-scores: z-score mitigates individual scoring biases by normalization into a standard normal variable with zero mean and one standard deviation. TANGOFLUS demonstrated the highest performance across both metrics, with z-scores of 0.2486 for OVL and 0.6919 for REL. This indicates its superior quality and strong alignment with the input prompts. Conversely, Audi LLM. 2 scored the lowest with z-scores of -0.3020 (OVL) and -0,4936 (REL), suggesting both lower sound quality and weaker adherence to textual inputs as compared to the other models.
Ranking: Ranks provide ordinal measure of performance, complementing z-score. TANGOFL.X
achieved the best rank with a mean rank of 1.7 (OVL) and 1.1 (REL), and mode ranks of 2 (OVL) and 1 (REL), affirming its superiority in subjective evaluations. In contrast, AudioLDM 2 consistently ranked lowest, with mean ranks of 3.5 (OVL) and 3.7 (REL), and mode ranks of 4 for both metrics. stableAudio and Tango had similar mean ranks for OVL (2.4), but Tango outperformed StableAudio on REL (mean ranks: 1.9 vs 3.3). Notably, St StableAudi 's bimodal OVL ranks (modes) and 3) suggest polarized annotator perceptions, likely due to misalignment between prompts and outputs, as reflected its REL rankings (mean 3.3, mode 3).
Elo scores provide probabilistic measure of model performance, by accounting for pairwise relative performance. Here, TANGOFLUX again excelled, achieving the highest Elo scores for both OVL (1,501) and REL (1,628). The Elo results highlight the robustness of TANGOFLUX, as it consistently outperformed other models in pairwise comparisons. Tango 2 emerged as the second- best performer, with Elo scores of 1,419 (OVL) and 1,507 (REL). Stabl eAudi o follows, showing competitive performance in OVL (1,444), but weaker REL score (1,268). Like other metries, AudioLDM 2 ranked last with the least Elo scores (1,236 for OVL and 1,196 for REL).
Elo Scores:
4.3 CRPO BEATS STATIC PREFERENCE DATASETS
To show the superiority of CRPO, compare its performance with two other static audio preference datasets: Audio-Alpace (Majumder et al., 2024) and BATON (Liao et al., 2024) (see Appendix A.13 for details).
We apply preference optimization to TANGOFLUX-base for one iteration since Audio-Alpaca and BATON are fixed datasets. Table reports objective metries FDopenl3. KLpasst- CLAP score and human evaluation results. Since all these models are variants TANGOFLUX, we conducted human evaluation using 50 prompts from the AudioCaps test set.

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Table 2: Human evaluation results on OVL and REL; SA Open Stable Audio Open.
z-scores
Ranking
Elo
Model
OVL
REL
OVL
REL
OVL
REL
Mean
Mode
Mean
Mode
AudioLDM
-0.3020
-0.4936
3.5
3.7
4
1,236
1,196
Open
0.0723
-0.3584
2.4
1.3
3.3
3
1,444
1,268
Tango -0.019
0.1602
2.4
2
1.9
2
1,419
1,507
TANGOFLUX 0.2486
0.6919
1.7
2
1.1
1
1,501
1,628
Table 3: Comparison of TAngOFLUX checkpoints aligned with different preference datasets. Along with objective metrics, we report human evaluation results 7-scores and Elo ratings. We embolden the best and underline the second-best scores for each metric.
Model
Objective Metries
z-scores
Elo
openIB
CLAP score
KL
Kasst
OVL
REL
OVL
REL
TANGOFLUX
75.1
0.480
1.15
0.17
0.18
1,546
1,520
TANGOPLUX-CIpo-1
79.1
0.453
1.18
0.12
0.07
1,446
1,467
TANGOFLUX-base
80.2
0.43I
1.22
-0.06
-0.21
1,325
1,253
TANGoFLoX-alpaca
80.0
0.448
1.20
-0.02
-0.00
1,428
1,366
TANGoFLuX-baton 80.5
0.437
1.20
-0.21
-0.04
1,253
1,392
Our results demonstrate that preference optimization with the CRPO dataset outperforms both Audio-
Alpaca and BATON across all objective and subjective metries. This highlights that CRPO a highly effective approach for constructing audio preference datasets for optimization. TANGOFL. UX also shows superior performance over T TANGOFFLUX- L.U-FL.U--rpol in both subjective and objective benchmarks, highlighting the effectiveness of the iterative process in CRPO.
4.4 BATCHED ONLINE DATA GENERATION IS KEY
12.5
0.48
1.3
saA
0.47
0.46
1.2
11.5
0.45
Iteration
Tteration
CRPO CRPO-oollise
Figure 2: Trajectory CLAPscore IS and KLpasst across training iterations: offline training peaks early at the peaking CLAP CLAP inceeeainnn KL and decreasing IS in contrast, the CLAP score of online training keeps increasing until iteration 4, while KL has a clear downward trend. The IS value of online training also shows gradual upward trend.
In Fig. 2, we present the results of five training iterations of CRPO, both with and without generating new data at each iteration. Our findings suggest that training on the same dataset over multiple iterations leads to quick performance saturation and eventual degradation. Specifically, for offline CRPO, the CLAP score decreases after the second iteration, while the KLpass increases significantly. The also peaked at iteration 2 of the offline CRPO and decreases after that. By the final iteration, the performance degradation is evident from the CLAP scores KL and IS scores that worse than in the first iteration, emphasizing the limitations of offline data. In contrast, the online CRPO with data generation before each iteration outperforms the offline CRPO w.r.t. CLAP score KI passt and IS.
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This performance degradation could be ascribed to reward over-optimization (Rafailov al., 2024a; Gao et al., 2022). Kim et al. (2024a) showed that the reference model serves as a regularizer in DPO training for language models. Several iterations of updating the reference model with the same dataset thus may hamper the due regularization of the loss. In Section 4.5, we show the paradoxical performance degradation with loss minimization, indicating over-optimization.
4.5
LCRPO VS LDPO-FM
14
0.16
LoPo.FM
1.3
Ermoo
0.14
0.12
1
3
2
5
2
5
Iteration
Iteration
Iteration
(a) CLAP soore
(h) FD Poopenl3
(c) KI pussi
Figure 3: Comparing DDOO.FM FM and CCRO w.r.t. (a) CLAP (b) FD Oppent3 and (c) KL across
iterations.
1.65
Winning Loss (CRPO)
Losing Loss (CRPO)
Loss
1.6
Winning Loss (DPO FM)
Losing Loss (DPO.FM)
1.55
1
3
Iteration
Figure 4: Winning and losing losses of DDPO-FM and CeRPO across iterations. Both losses increase with their margin.
To study the relationship between the winning and losing losses of LCRpO and LDPO FM (see Eq. (2)), we calculate the average winning and losing losses of the final checkpoint (epoch of each iteration on the training set. The losses plotted in Fig. 4. Simultancously in Fig. 3, we present the benchmark performances the checkpoints by LCRPO and LDPO-FM on AudioCaps training set. Here, only fixed preference data by TANGOFLUX-base.
As shown in Fig. 4, the winning and losing losses of both LERPO and CDPO-FM increase with each iter- ation, along with their differenncemarggnn. Despite the increase in losses, Fig. shows that benchmark performance improves, with LERPO achieving superior results in CLAP while maintaining similar KI KL and upppe across all iterations. We observe notable acceleration in loss growth from LDPO-FM after iteration 3, which may indicate performance saturation or degradation. In contrast, LCRPO exhibits more gradual and stable increase in loss, maintaining a smaller margin and more controlled growth, leading to less performance degradation as compared to LDPo-FM- This highlights the role of the winning loss as regularizer of the optimization dynamies by preventing the increase in margin at the cost of unmitigated increase of both winning loss and losing loss.
Our findings of increase in winning and losing losses in tandem with the margin is consistent with aligning LLMs with DPO (Rafailov et al., 2024b). This paradoxical performance improvement from both LERrO and FMPO is also noted by Rafailov et (2024a) in the context LLMs.
4.6 INFERENCE TIME VS PERFORMANCE
TANGOFLUX beats the other models in terms of performance per unit of inference time, measured w.r.t. CLAP score and FD, FDopent3. For fair comparison of inference time, we use a batch size of for all inference some models do not support batch inference. See Appendix A.9 for more details.

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Published as a conference paper at ICLR 2026
5 RELATED WORKS
Text- Sext-To-Audio Generation. TTA Generation has lately drawn attention due to AM (Liu et al., 2024b; 2023), Tango (Majumder et al., 2024; Ghosal et al., 2023; Kong et al., 2024), and Stable Audio (Evans et al., 2024a;c;bb series models. These adopt the diffusion framework (Song & Ermon, 2020; Rombach et al., 2022; Song al., 2022; Ho al., 2020), which trains latent diffusion model conditioned on textual embedding. Another common framework for TTA generation flow matching which was employed in models such as VoiceBox (Le al., 2023), AudioBox (Vyas et al., 2023), FlashAudio (Liu et al., 2024c). ETTA (gil Lee et al., 202 (Ali et al., 2024) highlights the benefits of scaling both data-using using synthetic captions -nndss-addddddd model size enhance TTA generation performance. Whereas Audio AudioTurbo(Zhaee et al., 2025), IMPACT(Huang et al., 2025) also explored generating high quality audio with fast inference speed.
Alignment Method. Preference optimization is the standard approach for aligning LLMs, achieved either by training a reward model capture human preferences (Ouyang et al., 2022) or by using the LLM itself as the reward model (Rafailov et al., 2024c). Recent advances improve this process through iterative alignment, leveraging human annotators to construct preference pairs or utilizing pre-trained reward models. (Kim et al., 2024a; Chen al., 2024; Gulcehre et al., 2023; Yuan et al., 2024). Verifiable answers can enhance the construction of preference pairs. For diffusion and flow-based models, Diffusion-DPO shows that these models can be aligned similarly (Wallace et al., 2023). However, constructing preference pairs for TTA is challenging due to the absence of "gold" audio for given text prompts and the subjective nature of audio. BATON (Liao al., 2024) relies human annotations, which is not scalable.
CONCLUSION
We introduce TANGOFLUX, a flow-based text-to-audio model aligned using synthetic preference data generated online during training. Objective and human evaluations show that TANGOFLIN produces audio more representative of user prompts than existing diffusion-based models, achieving state-of-the-art performance with significantly fewer trained parameters. These advancements make TANGOFLUX practical and scalable solution for widespread adoption.
ETHICAL CONSIDERATIONS
There is possibility of misuse of the audio generation model in fabricating harmful multimedia content.
REPRODUCIBILITY STATEMENT
We shall publicly release the implementation of model training, inference, and evaluation upon acceptance. We also mention the hyperparameters in the appendix. An anonymized implementation shared in the supplementary materials.
ACKNOWLEDGEMENT
This work is supported by the National Research Foundation, Singapore, under its National Large Language Models Funding Initiative (AISG Award No: AISG-NMLP-20224005)) NTU SUG project #025628-00001.
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A APPENDIX
A.1
THE USE OF LARGE LANGUAGE MODELS
We employed LLMs to assist with two key tasks: ensuring grammatical accuracy and improving the paper's narrative flow.
A.2 AUDIO SAMPLE COUNT PER PROMPT VS TTA PERFORMANCE
In this ablation study on the imitial iteration of TangoFlux-bes base, we compared generating N audio samples per prompt for preference dataset creation (by pairing the worst and best outputs) where N = 2, 5, 10. All setups were trained for 8 epochs from TangoFlux-base and we tracked the CLAP, FD, and KL
uuing the maximum CLAP score per epoch for evaluation. The results in Table 4 show slightly better performance (higher CLAP score, lower FD) when using more samples. The decision on the optimal N largely depends on the specific application and available resources. computation cost a limiting factor, opting for lower N might be worthwhile compromise.
Table Audio sample count vs TTA performance.
10
CLAP
0.443
0.453
0.455
FD
80.0
79.1
77
KI.
1.24
1.18
1.21
A.3 CLAP AS A REWARD MODEL
To validate CLAP as proxy reward model for evaluating audio output, we further evaluate TANGOFLUX under CLAP-driven Best-of-N policy, where N E {1,5, 10, 15). We use CLAP 630k-au Baok--uddioe--bba besttberttttt checkpoint to rank the generated audios. The results in Table 5 suggest that increasing yield better CLAP and KLpasst while FDopen13 remains about the same. This indicates that the CLAP can identify well-aligned audio outputs that better represent the textual descriptions, without compromising diversity or quality, as implied by the lower KI passs and similar FD FDopen13.
Table 5: Best-of-N FD, KL, and CLAP score.
Model
N
FD Dppent3 4
KL KL 4
CLAP scome 1
75.0
1.15
0.480
5
74.3
1.14
0.494
TANGOFLUX
75.8
1.08
0.499
75.1
1.11
0.502
108.4
1.11
0.447
108.5
1.05
0.467
Tango
10
108.4
1.08
0.474
15
108.7
1.06
0.473
A.4 RECTIFIED FLOWS
Given latent representation audio sample v1, noise sample xi ~ ~N I), time-step / [0, 1), we can construct training sample I; where the model learns to predict a velocity 8, ds that guides Z1. While there exist several methods of constructing transport path we used rectified flows (RFs) (Liu et al., 2022). in which the forward process are straight paths between target distribution and noise distribution. defined in Eq. (4). It is empirically shown that rectified flows more sample efficient and degrade less than other formulations, while consuming fewer sampling steps (Esser al., 2024). The model u(x,, 1; 0) directly regresses the ground truth velocity v, using
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Table TANGOFLUX with different classifier free guidance (CFG) values.
Model
Steps
CFG
FD Fpppel3
Kl Kussst
CLAP score 1
50
3.0
77.7
1.14
0.479
50
3.5
76.1
1.14
0.481
TANGOFLUX
50
4.0
74.9
1.15
0.476
50
4.5
75.1
1.15
0,480
50
5.0
74.6
1.15
0.472
the flow matching loss Eq. (5).
dxt
3t = (1 - 1)21 + kTo, 1t
3o 31
(4)
di
LrM Ex, mpo ||( 0) 1s
(5)
A.5 MODEL TRAINING
We pretrained TANGOFLUX on Wavcaps (Mei et al., 2024) dataset for epochs with the AdamW (Loshchilov & Hutter, 2019), 31 0.9, B2 0.95, a max learning rate of 5 x 10-1 We used a linear learning rate scheduler 2000 steps. We used five A40 GPUs with a batch size of 16 each device, resulting in overall batch size of 80. After pretraining, T TANGOFLE FLUX was finetuned on the AudioCaps training set for 65 additional epochs. Several works find that sampling timesteps from the middle of its range [0, leads to superior results (Hang al., 2024; Kim et al., 2024b; Karras et al., 2022), thus, we sampled t from a logit-normal distribution with a mean of 0 and variance of 1, following the approach in (Esser et al., 2024). We name this version as
During the alignment phase, we used the same optimizer, but overall batch size of 48, maximum learning rate of 10-5 and linear warmup of 100 steps. For each iteration of CRPO, we train for epochs and select the last epoch checkpoint to perform batched online data generation. We performed 5 iterations of CRPO due to the manifestation of performance saturation.
A.6 EFFECT OF CFG SCALE
We conduct an ablation of the effect of CFG scale for TANGOFLUX and show the result in Table 6. reveals trade-off:: higher CFG values improve FDopent score (lower FDopent3) but slightly reduce semantic alignment (CLAP score), which peaks at CFG=3.5. The results emphasize CFG=3.5 as the optimal balance between fidelity and semantic relevance.
A.7 HEUN VS EULER SOLVER
Unlike straightforward Euler solver, Heun solver uses second order look-ahead to better estimate trajectory. However, get slightly worse results shown in Table 7. We speculate that it could be due to overoptimization, analogous to greedy decoding for token generation.
Table 7: Comparison between Heun and Euler solver for integration over velocity.
Heun
Euler
NFE
100
100
CLAP
0.474
0.480
KL
1.21
1.16
FD
75.3
75.1
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Generated Audio
Ideal Match
25
Measured Duration
15
5
5
10
20
25
Expected Duration
Figure 5: Expected input duration vs generated duration plot measured by an energy-based silence detection approach.
A.8 ACCURACY OF DURATION CONTROL
We conducted an analysis to measure the accuracy of duration control and we adopted the approach (Evans al., 2024a) to measure the actual generated audio duration given requested duration. We generated 100 audio samples each conditioned textual prompts with requested durations of 5, 10, 15, 20, and 25 seconds. Since the generated audio is cropped at the requested duration, the actual duration of the content cannot exceed the requested duration. However, the model may introduce preceding silence within the generated sample. To accurately determine the actual duration of the non-silent, generated content, employed energy-based silence detection method on the audio waveforms. We visualize the scatter plot in Figure 5. Our plot suggests that most of the generated audios adhere exactly the requested duration, with some minor exceptions.
A.9 INFERENCE TIME VS PERFORMANCE COMPARISON
sere Across models, we compare the trajectory of CLAP score and FD score with increasing inference time for steps 10, 25, 50. 100, 150, and 200, as shown in Figure 6. TANGOFLUS FUX demonstrates remarkable balance between efficiency and performance, consistently achieving higher CLAP suore and lower FD, FDpeent scores while requiring significantly less inference time compared other models.
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Inference Time CLAP Score
Inference Time vs FD Score
Tangoflux
lango AudiaLDM
CLAP Score 1
FD Soora
Tango
Stabll Audio
pugo
AudioLDM
100 mapa
Stable Audio Open
130 deee
200 ciepe
Taug
Inference Time (6)
Inference Time (e)
(a)
(b)
Figure 6: Comparison of (a) CLAP score and (b) FD FDopen13 Inference Time for each model. Results are plotted for step counts of 10, 25, 50, 100, 150, and 200.
For example, at 50 steps, TANGOFLUX achieves a CLAP score in just 3.7 seconds. In comparison, Stable Audio Open requires 4.5 seconds for the same step count but only achieves a CLAP of 0.284 (41% lower than TANGOFLUX) and an FDopen13 score of 87.8 (17% worse than TANGOFLUX), This demonstrates that TANGOFLIE achieves superior performance metrics in less time. Additionally, at a lower step count of 10, TANGOFLUS maintains strong performance with a CLAP of 0.465 and FDeee13 score of 77.2 in just 1.1 seconds. In contrast, Audio o dm2 at the same step count achieves a lower CLAPssome of 0.357 (23% lower) and significantly worse FDDeee13 score of 131.7 (70% higher), while requiring 1.5 seconds (36% more time). We also observe that reducing the step count from 200 to 10 has a minimal impact on TANgoF Lux's performance, highlighting its robustness. Specifically, TANGO decreases by only 3.2% (from 0.480 to 0,465), and its FDopen13 score increases by only 4.5% (from 73.9 to 77.2). In contrast, Tango 2 shows larger degradation, with its CLAP decreasing by 16.0% (from 0.443 to 0.372) and its FD opopn13 score increasing by 37.8% (from 108.4 to 158.6).
score of 0.480 and an Oponn13 score of 75.1
CLAP score
These results highlight TANGOFLuX's effectiveness delivering high-quality outputs with lower computational requirements, making it a highly efficient choice for scenarios where inference time is critical.
A. OBJECTIVE EVALUATION METRICS
KAD and FDopen13 both evaluates the similarity between the statistics of a generated audio set and another reference audio set in the feature space. A low KAD, FDopeat3 indicates realistic audio that closely resembles the reference audio. KI Kl computes the KI. divergence over the probabilities of the labels between the generated and the reference audio given the state-of-the-a audio tagger PaSST. low KLpast signifies the generated and reference audio share similar semantics tags, CLAP score is reference-free metric that measures the cosine similarity between the audio and the text prompt. High CLAPscom score denotes the generated audio aligns with the textual prompt. IS measures the specificity and coverage of a set of samples. A high IS score represents the diversity in the generated audio. We use stable-audio lale--audo---teieeee metrics (Evans et al., 2024a) to compute FD FDopenl3. KLpasstt CLAP score and AudioL_DM evaluation toolkit (Liu al., 2023) compute IS. For KAD evaluation, we the (WaveGram-LLogMee MelM embedding model, which has shown the
kadtk toolkit with the PANNs-WGII highest correlation with human judgment. Note that we use different CLAP checkpoints create our preference dataset (630k-audhl audioset-bestt and to perform evaluation 2330k-audioset-fusseee bbst)) These results are indicated Table as CLAP
CLAPscure- scure-
A.11 CRPO VS GRPO
We have considered GRPO in our alignment objective. However, GRPO is fully online algorithm which is computationally expensive. Hence, we conducted an experiment of optimizing GRPO loss
'ttpps: //tg::///uggggg aagaeenfaccecc.o htt///kewss/aaion_ee. 630k audi oset best
ob/main//
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static dataset that was constructed with TANGOFLUX-bass (20k samples of 5 generations each, group size of 5). Our results show that the performance slightly worse than running CRPO for iteration.
Table 8: Comparison between CRPO and GRPO.
CRPO
GRPO
CLAP
0.453
0.448
KL
1.26 1.18
FD
79.1
79.6
A.12 HUMAN EVALUATION
The human evaluation was performed using web-based Gradio app. Each annotator was presented with 20 prompts, each having four audio samples generated by four distinct text-to-audio models, shuffled randomly, as shown in Fig. 7. Before the annotation process, the annotators were instructed with the following directive:
Welcome username
Instructions for evaluating audio clips
Please carefully read the instructions below.
## Task
You evaluate four 10-second- audio outputs each of the 20 prompts below. These four outputs are from four different models. You are to judge each output with respect to two qualities:
Overall Quality (OVL): The overall quality of the audio is to be judged on scale from 0 to 100: being absolute noise with no discernible feature. Whereas, 100 is perfect. Overall fidelity, clarity, and noisiness of the audio are important here.
Relevance (REL): The extent of audio alignment with the prompt is to be judged on scale from 100: with 0 being absolute irrelevance the input description. Whereas, 100 is perfect representation of the input description. You are to judge if the concepts from the input prompt appear the audio in the described temporal order.
You may want to compare the audios of the same prompt with each other during the evaluation.
## Listening guide
1. Please use head/carphone to listen to minimize exposure to the external noise.
2. Please move to quiet place as well, if possible.
## UI guide
1. Each audio clip has two attributes OVL and REL below. You may select the appropriate option from the dropdown list.
To save your judgments, please click any of the buttons. All the save buttons function identically. They are placed everywhere to avoid the need to scroll to save.
Hope the instructions were clear. Please feel free to reach out to us for any queries.
https//wwwwgradio.app
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Adwly puta atcantrrolllly by of wholk coaites playo
x
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8K
Audo
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600
0:22
6:00
020
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Figure 7: The Gradio-based human evaluation form created for the annotators to score the model generated audios with respect the input prompts.
A.12.1 EVALUATION DATASET
To evaluate the cdaructiio-followinn capabilities and robustness of TTA models, created 50 distriiiitiiittion complex captions, such as "A pile of coins spills onto a wooden table with metallic clatter, followed by the hushed murmur of a lavern crowd and the creak of a swinging door". These captions describe 3-6 events and aim to go beyond conventional or overused sounds in the evaluation sets, such simple animal noises, footsteps, city ambiance. Events were identified using GPT4o to evaluate the captions generated. Each of the generated prompts contains multiple events including several where the temporal order of the events be maintained. Details of our caption generation template and samples of generated captions can be found in the Appendix A.12.
A.12.2 HUMAN EVALUATION METRICS
We report three key metrics for subjective evaluation:
z-score: The average of the scores assigned individual annotators. Due the subjective nature of these scores and the significant variance observed in the annotator scoring patterns, the ratings were normalized z-scores the annotator level: 2s (8ty p4)/a, Z,y: The z-score for annotator i's score of model My. This is the score after applying z-score normalization. Syy: The raw score assigned by annotator to model j. This the original score before normalization. p4z The mean score assigned by annotator across all models. It represents the central tendency of the annotator's scoring pattern. a;: The standard deviation of annotator i's scores across all models. This measures the variability spread in the annotator's 's ratings.
This normalization procedure adjusts the raw scores, centering them around the annotator's mean score and scaling by the annotator's score spread (standard deviation). This ensures that scores from different annotators are comparable, helping to mitigate individual scoring biases.
Despite z-score normalization, the variability in annotator scoring still introduce noise into the evaluation process. To address this, models are also ranked based on their absolute scores. We utilize the mean (average rank of model), and mode (the most common rank of model) as metrics for evaluating these rankings.
Ranking:
Elo: Elo-based evaluation, widely adopted method in language model assessment, involves pairwise model comparisons. We first normalized the absolute scores the models using z-score normalization and then derived Elo scores from these pairwise comparisons. Elo score mitigates the noise and
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inconsistencies observed in scoring and ranking techniques. Specifically, Elo considers the relative performance between models rather than relying solely on absolute or averaged scores, providing a more robust measure of model quality under subjective evaluation. While ranking- basdd evaluation provides an ordinal comparison of models, determining the order of performance (e.g.. Model A ranks first, Model B ranks second), it does capture the magnitude of differences between ranks. For instance, if the difference between the first and second rankers is minimal, this is not evident from ranks alone. Elo scoring addresses this limitation by integrating both ranking and pairwise performance data. In ranking-bases systems, the rank R₃ of model M, is determined purely by its position relative others:
R4 position of M, in the sorted list of models based performance.
However, this approach fails to quantify: 1) The gap in performance between consecutive ranks. 2) The consistency of relative performance across different pairwise comparisons. Elo scoring provides probabilistic measure of model performance based on pairwise comparisons. By leveraging annotator scores, Elo assigns continuous score E; each model M, capturing its relative strength.
A.12.3 PROMPTS USED IN THE EVALUATION
Table 9: Prompts used in human evaluation and their characteristics.
Prompts
Multiple Events
Temporal Events
A robotic arm whirs frantically while an electric plasma are crackles and metallic voice counts down ominously, interspersed with glass vials clinking to the floor.
Unfamiliar chirps overlap with a low, throbbing hum as bioluminescent plants audibly crackle and squelch with movement.
x
Dripping water echoes sharply, distant growl rever- berates through the cavern, and soft scraping metal suggests something lurking unseen.
Alarms blare with rising urgency as fragments clatter against metallic hull, interrupted by a faint hiss of escaping air.
/
Hundreds of tiny wings buzz with chaotic pitch shift, joined by the faint clattering of mandibles and or- ganic squish as they collide.
Jagged rocks crumble underfoot while distant ocean waves crash below, punctuated by the sudden snap of rope.
Digital beeps and chirps meld with overlapping chatter in multiple languages, as automated drones whiz past, scanning barcodes audibly.
Rusted swings creak in rhythmic disarray, faint me- chanical laugh stutters from a distant speaker, and the sound of gravel crunches under unseen footsteps.
x
Bubbling lava gurgles ominously, instruments beep irregularly, and faint crackling signals static from failing radio.
Tiny pops and hisses of chemical reactions intermingle with the thythmic pumping of centrifuge and the soft whir of air filtration.
/
x
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The faint hiss of a gas leak grows louder as metal chains rattle and a single marble rolls across the floor.
A hand slaps a table sharply, followed by the shuffle of playing cards and the hum of overhead fan.
A train horn blares in the distance as a bicycle bell chimes and soda can pops open with fizzy hiss.
drawer creaks open, papers rustle wildly, and the sharp click of lock snapping shut echoes.
burst of static interrupts soft typing sounds, followed by the distant chirp of pager and cough.
heavy book thuds onto a desk, accompanied by the faint buzz of fluorescent light and muffled sneeze.
The sharp squeak of sneakers on a gym floor blends with the rhythmic bounce of basketball and the screech of a metal door.
An elevator dings, its doors sliding open, muffled voices overlap with the shuffle of heavy bags.
A clock ticks steadily, light switch clicks on, and the crackle of a fire igniting briefly fills the silence.
A fork scrapes a plate, water drips slowly into sink, and the faint hum of a refrigerator lingers in the back- ground.
cat hisses sharply as glass shatters nearby, followed by hurried footsteps and the slam of closing door.
parade marches through a town square, with drum- beats pounding, children clapping, and horse neigh- ing amidst the commotion.
A basketball bounces rhythmically court, shoes squeak against the floor, and a referee's 's whistle cuts through the air.
/
x
baby giggles uncontrollably, a stack of blocks crashes to the ground, and the faint hum of lullaby toy plays in the background.
The rumble of subway train grows louder, followed by the screech of brakes and muffled announcements over crackling speaker.
beekeeper moves carefully as bees buzz intensely, smoker puffs softly, and wooden frames creak they're lifted.
dog shakes off water with noisy splatter, bicycle bell rings, and a distant lawnmower hums faintly in the background.
Books fall off' a shelf with a heavy thud, chair scrapes loudly across a wooden floor, and a surprised gasp echoes.
x
A soccer ball hits goalpost with a metallic clang, followed by cheers. clapping, and the distant hum of commentator's voice.
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A hiker's pole taps against rocks, a mountain goat bleats sharply, and loose gravel tumbles noisily down a steep slope.
rooster crows loudly at dawn, joined by the rustle of feathers and the crunch of chicken feed scattered on the ground.
/
A carpenter saws through wood with steady strokes, hammer strikes nails rhythmically, and measuring tape snaps back with metallic zing.
x
frog splashes into a pond as dragonflies buzz nearby, accompanied by the distant croak of toads echoing through the marsh.
The crack of a whip startles a herd of cattle, their hooves clatter against dirt path as rancher shouts commands.
x
paper shredder whirs noisily, the rustle of documents being fed in grows louder, and stapler clicks shut in rapid succession.
/
An elephant trumpets in the savanna as a herd stomps through dry grass, accompanied by the buzz of flies and the distant roar of a lion.
/
x
mime claps silently as a juggling act clinks glass balls together, and crowd bursts into laughter at the clatter of dropped prop.
A train conductor blows sharp whistle, metal wheels screech on the rails, and passengers murmur while settling into their seats.
/
squirrel chitters nervously as acorns drop from tree, landing with dull thuds, while leaves rustle above in quick bursts movement.
A blacksmith hammers molten iron with rhythmic clangs, a bellows pumps air with a whoosh, and sparks sizzle on stone floor.
x
skateboard grinds loudly against a metal rail, fol- lowed by the sharp slap of wheels hitting pavement and a triumphant cheer from the rider.
An old typewriter clacks rapidly as paper rustles with each keystroke, interrupted by the sharp ding of the carriage return.
x
pack wolves howls unison as dry leaves crunch underfoot, and the faint trickle of nearby stream echoes through the forest.
/
A. 13 BATON AS A PREFERENCE DATASET
BATON contains auman-annotated data where annotators assign binary label of 0 or I to each audio sample based on its alignment with a given prompt: indicates alignment, while 0 indicates misalignment. We construct preference dataset by pairing audio samples labeled I (winners) with those labeled 0 (losers) for same prompt, creating set of winner-losee pairs.
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Reviews:
[{"review_id": "RKWtGLwESY", "reviewer": "Reviewer_w4XM", "summary": "The paper proposes text-to-audio (TTA) alignment via DPO. The core idea is to curate a synthetic preference dataset using CLAP: after training an audio generator, the system samples N audios per prompt, selects a winner/loser by CLAP similarity, and forms triplets for DPO. The authors iterate this process and additionally propose keeping a flow-matching loss on winners alongside DPO (denoted 𝐿_CRPO). A flux-like architecture is adopted as the backbone.", "questions": "- in Figure 2, since CLAP is used to construct preferences, reporting FAD or IS would help understand more the gains.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "- Clear and readable paper.\n- The method is intuitive. the evaluation and metrics follows standard evaluation.\n- the authors promised to release code and model weights is valuable for the community.", "weaknesses": "**Novelty:** the authors main contribution which is CLAP-driven automatic preference curation for DPO is closely related to Tango-2 and CLIP-DPO as mention in L155-L160, with the main differences being that Tango-2 and CLIP-DPO operates on a static dataset. Running the same loop for multiple iterations seems to me an incremental extension rather than a novel contribution. \n\n**Evaluation:** the base model is fine-tuned on AudioCaps (L833), which I believe would bias the results on AudioCaps and undermines comparisons to open-source baselines (Stable Audio Open, AudioLDM2). An out-of-distribution evaluation or reporting pre-finetuning numbers for all models would help understanding the effectiveness of the proposed method.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "E91yicsghq", "reviewer": "Reviewer_nhq3", "summary": "This study introduces an iterative alignment framework named CRPO to address the challenges Text-to-Audio (TTA) models face in adhering to complex prompts. The method employs a self-improvement loop, leveraging the CLAP model as a proxy reward model to automatically generate and curate preference data. Furthermore, the authors enhance the traditional Direct Preference Optimization (DPO) loss function by incorporating a regularization term to stabilize the training process. Ultimately, they train and open-source a highly efficient and high-performance TTA model called TANGOFLUX based on this method, which demonstrates state-of-the-art performance across multiple evaluations.", "questions": "None", "limitations": "", "rating": 4, "confidence": 2, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "The most significant strength of this work is its delivery of a practical and scalable alignment solution for the TTA domain, which lacks off-the-shelf tools. It cleverly repurposes the CLAP model as an effective reward model, addressing the core bottleneck of preference data creation. Technically, the introduction of the LCRPO loss function demonstrates a deep understanding of the potential pitfalls in preference optimization and presents a robust solution. Finally, releasing an open-source, state-of-the-art model trained on public data not only advances the field but also provides an invaluable resource and benchmark for future research.", "weaknesses": "Over-reliance on the Proxy Reward Model: The alignment process is inherently constrained by the capabilities of the CLAP model, as its potential biases and limitations as a proxy reward model could be amplified during iterative training.\n\nRisks of Self-Correction and Limited Generalization Testing: The self-improvement cycle risks reinforcing its own flaws, and the model's generalization is evaluated on a relatively small out-of-distribution dataset, which may not be fully conclusive.\n\nLimited Novelty: The proposed method is not fundamentally novel, as it primarily combines and applies existing, mature technologies to a new domain rather than introducing a new core algorithm.\n\nLack of Polish in Presentation: The manuscript contains minor typographical errors, such as a quotation mark issue on line 263, which may suggest a need for more thorough proofreading.\n\nThe referee acts as the athlete; CLAP RL; CLAP testing.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "SNHeRDlmoI", "reviewer": "Reviewer_ddb1", "summary": "This paper proposes a state-of-the-art in-the-wild audio generation model called TangoFlux. The innovations are two-fold:\n- A flow-matching model based on FluxTransformer and MMDiT that can generate high-quality audio with variable duration.\n- A fine-tuning method called CRPO that alternates between generating preference datasets and using DPO to learn from that dataset.", "questions": "- Which encoder did the KAD metric use? If it is the same encoder as the FD metric, did you find KAD better/more correlated with subjective quality than FD, or worse/less?\n- CRPO significantly improves subjective REL. Is there an analysis on what aspects of prompt following have been improved (i.e., temporal relationship of audio events, duration control, etc.)? Do you think conditioning effectiveness can also be improved via CFG?\n- TangoFlux allows for duration control. How accurate is this control? Does duration control affect generation quality? Would it be possible to present a scatter plot between the requested duration and the actual generated length?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "Overall, I found this paper nice and interesting to read, and I learned a lot from it.\n\nI really like the ablation study between CRPO loss and DPO loss (Figure 3) as well as other settings (Table 3). Super intuitive and informative.", "weaknesses": "The paper is already in good overall shape. That said, there are some remaining weaknesses:\n- Human evaluation is performed with 50 human-created and GPT-4o-generated prompts (Section 3.4). This prompt bank seems somewhat small, especially because the 0.2486 OVL z-score of TangoFlux is moderate compared to existing work. Would it be possible to increase the scale of subjective evaluation? If not, some confidence interval or hypothesis testing analyses would greatly clarify the quality advantage of TangoFlux over existing work.\n\nI would also appreciate it if the authors could respond to my questions below.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "igSoVzzy7X", "reviewer": "Reviewer_t9Xr", "summary": "This work introduces TangoFlux, a TTA model trained on open data and aligned via CLAP-Ranked Preference Optimization (CRPO). CRPO performs an online, self-improving preference-optimization loop that ranks multiple candidates per prompt with CLAP and optimizes rectified-flow models accordingly.", "questions": "The batch size used for measuring inference time is not explicitly stated.", "limitations": "", "rating": 4, "confidence": 5, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "- Applying online data generation outperforms baselines that use static preference datasets.\n- The system can generate up to 30 seconds of audio at 44.1 kHz, an uncommon setting, as most text-to-audio models produce ~10 seconds at 16 kHz.", "weaknesses": "Lacking metrics of FD using PANNs. This metric is the most common metric used by TTA models for evaluating fidelity. (The paper only reports FD using OpenL3)\n\nThe evaluation section is missing comparisons to several publicly available state-of-the-art Text-to-Audio models. Specifically, an updated comparison should ideally include results for:\n- Make-An-Audio 2 [A]\n- EzAudio [B]\n- ConsistencyTTA [C]\n- AudioLCM [D]\n\nParticularly, ConsistencyTTA and AudioLCM also target on fast inference with consistency models.\n\n[A] Huang, Jiawei, et al. \"Make-an-audio 2: Temporal-enhanced text-to-audio generation.\" arXiv preprint arXiv:2305.18474 (2023).\n\n[B] Hai, Jiarui, et al. \"Ezaudio: Enhancing text-to-audio generation with efficient diffusion transformer.\" arXiv preprint arXiv:2409.10819 (2024).\n\n[C] Bai, Y., Dang, T., Tran, D., Koishida, K., Sojoudi, S. (2024) ConsistencyTTA: Accelerating Diffusion-Based Text-to-Audio Generation with Consistency Distillation. Interspeech 2024\n\n[D] Liu, Huadai, et al. \"AudioLCM: Text-to-Audio Generation with Latent Consistency Models.\" CoRR (2024).\n\nFor variable duration, I think it is worth mentioning that the model always operates on a fixed-length latent space, but the duration conditioning explicitly controls how much of that latent space has actual content other than silence. Make sure readers know that this work is not an autoregressive model that can generate an arbitrary length of latent sequence.\n\nLacking references:\n\nThe Related Works section could be strengthened by citing influential but non-public models, such as AudioTurbo [E] and IMPACT [F], to provide a comprehensive overview of the field, similar to how ETTA and Audiobox are referenced. While direct performance comparisons to proprietary models are understandably impossible, acknowledging their existence in the literature is a key academic practice (This will not affect the overall ratings for this work, but it is nice to have).\n\n[E] Zhao, Junqi, et al. \"AudioTurbo: Fast Text-to-Audio Generation with Rectified Diffusion.\" arXiv preprint arXiv:2505.22106 (2025).\n[F] Huang, Kuan-Po, et al. \"IMPACT: Iterative Mask-based Parallel Decoding for Text-to-Audio Generation with Diffusion Modeling.\" ICML 2025", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)