DDSynth-RL: Audio Synthesizer Inversion via Discrete Diffusion with Reinforcement Learning

Daniel Chin; Gus Xia; Junan Zhang; Junyan Jiang; Tristan Wu; Yansen Jing

REVIEW 4 major objections 6 minor 47 references

DDSynth-RL: Audio Synthesizer Inversion via Discrete Diffusion with Reinforcement Learning

T0 review · 4 major / 6 minor · reviewed 2026-08-08 · deepseek-v4-flash

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Pith's one-line read The paper tries to establish that synthesizer inversion is best treated as conditional generation over discrete parameter tokens, with masked discrete diffusion as the generator and a second stage of GRPO-style fine-tuning on…

desk verdict A promising combination of masked discrete diffusion and GRPO for synthesizer inversion, but the OOD generalization claim rests on an evaluation set that overlaps with fine-tuning validation. read the letter →

arxiv 2608.03032 v1 pith:JIS2CUTP submitted 2026-08-04 cs.SD

Tristan Wu , Daniel Chin , Junan Zhang , Junyan Jiang , Yansen Jing , Gus Xia This is my paper · ORCID

classification cs.SD

keywords synthesizerinversiondiscretediffusionmaskedGRPOaudio-domainrewardDexedNSynthreinforcementlearning

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper tries to establish that synthesizer inversion — inferring a synth's parameter settings from an audio clip — is best treated as conditional generation over discrete parameter tokens, not as regression or continuous denoising. It argues that masked discrete diffusion avoids the fixed-order bias of autoregressive models and the continuous-relaxation mismatch of flow matching. A second stage fine-tunes the model with GRPO-style reinforcement learning using rewards computed from audio rendered by the black-box synthesizer, which improves out-of-domain audio matching. If true, this would give sound designers a practical way to match and edit real-world sounds without needing a differentiable synthesizer.

What carries the argument

The central object is a masked discrete diffusion model operating over a tokenized parameter sequence: 102 Dexed controls plus pitch, velocity, and duration are each mapped to a discrete vocabulary (quantized controls use 25 bins). Training masks random token subsets with a timestep-dependent probability and supervises only masked positions; inference starts from all-masked and fills one position per step, choosing the position with highest normalized-entropy confidence and the most likely token. A second mechanism is GRPO fine-tuning: candidate parameter sequences are decoded stochastically, rendered with the Dexed VST, scored by audio metrics (wMFCC, CLAP, CREPE, multi-scale spectrogram, spectral optimal transport, RMS envelope), and the policy is updated with group-normalized advantages.

What would settle it

Render a held-out set of audio from a different synthesizer or from real acoustic instruments, fine-tune the first-stage model with GRPO on those same held-out clips, and measure whether the audio-distance improvements persist on a separate third-party set; if the gains vanish or become negative, the OOD improvement is domain adaptation, not generalization.

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Extended reading notes

Core claim

On Dexed, a 102-parameter FM synthesizer with MIDI tokens, the paper's discrete diffusion model after supervised training is competitive with a carefully ordered autoregressive Transformer and clearly outperforms flow matching on most metrics; on the out-of-domain NSynth set it leads on several MFCC-based distances. Fine-tuning with GRPO and multi-metric audio rewards cuts several out-of-domain audio distances substantially (wMFCC from 10.04 to 5.96, for example) and further reduces CLAP distance when optimized with CLAP and CREPE rewards. The paper concludes that modeling categorical synthesizer controls directly in token space, then optimizing rendered-audio rewards, is an effective recipe for non-differentiable synthesizer inversion.

Load-bearing premise

The out-of-domain evaluation uses NSynth validation and test audio, but the model is fine-tuned on NSynth training audio, so the reported out-of-domain gains may come from fitting the NSynth timbre distribution rather than from generalizable audio-matching ability.

Editorial extensions

If this is right

Discrete diffusion avoids the need to hand-design a parameter generation order, which the paper shows matters: the autoregressive baseline degrades with random orders, while discrete diffusion is order-agnostic.
Because the renderer is treated as a black box, the same two-stage recipe should transfer to other non-differentiable synthesizers without changing the model family.
Optimizing audio-domain rewards directly can dramatically close out-of-domain gaps: on NSynth, multi-reward GRPO cuts wMFCC by roughly 40% relative to the first-stage model.
The in-domain metrics degrade after GRPO because the policy moves away from the Dexed preset distribution toward NSynth-like timbres, showing a trade-off between matching the training preset distribution and matching arbitrary input audio.
The paper's observation that slight overfitting can improve subjective quality suggests human-designed preset datasets carry an aesthetic prior worth preserving, not just a parameter-to-audio mapping.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

A stronger test of out-of-domain generalization would fine-tune on one synthesizer and evaluate on a genuinely different synthesizer's timbres, or on real instrument recordings; the current NSynth evaluation may reflect adaptation to the NSynth timbre distribution more than generalizable inversion skill.
The one-position-at-a-time decoding with entropy confidence could be extended to a 'wait' policy that decides when to stop refining, which the paper does not explore but its own trajectory formalism would support.
Because the reward is a weighted sum of audio metrics, a natural next direction is to replace hand-set weights with learned or preference-based rewards, e.g., from listener comparisons, which would connect to music-generation RL but is not tested here.
The paper's claim that discrete diffusion is order-agnostic suggests it may scale better to high-dimensional synthesizers with many interacting controls, but this is untested beyond Dexed.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

The thing to know: this is a genuinely new combination of masked discrete diffusion and GRPO with rendered-audio rewards for synthesizer inversion. The first-stage results are decent and the problem framing is clear. But the headline claim that GRPO improves out-of-domain matching is not actually supported by the evaluation as written.

What the paper does well: it treats synthesizer inversion as conditional generation over discrete tokens, which is a sensible response to the one-to-many mapping problem. The AR order ablation is a nice piece of evidence that discrete diffusion avoids a real design burden. The method section is readable, and treating the renderer as a black box with RL-style rewards is an appropriate and practical choice. The comparison with flow matching also shows a substantial gap, though that gap is confounded.

Where the soft spots are: first, no error bars or significance tests anywhere. Table 1 reports averages over 200 examples, so the differences between DD and AR (e.g., OOD wMFCC 10.04 vs 10.85) may be noise. Second, the FM baseline uses a different audio encoder and architecture than AR and DD, so the FM comparison attributes the gap to continuous relaxation when architecture is a plausible alternative explanation. Third, and most important, the GRPO OOD claim is weakened by the evaluation setup. NSynth train is used for fine-tuning, NSynth valid is used for validation, and the OOD rows in Table 1 appear to come from that same validation set. That means the reported improvements may reflect in-distribution reward optimization and selection-on-validation, not generalization to truly unseen timbres. The stress-test note is right on this point. Fourth, the reward metrics overlap with the evaluation metrics; that is not circular in a technical sense, but it does mean the eval numbers are measuring exactly what the policy was trained to optimize, which undercuts any generalization interpretation.

The paper is still worth engaging with. The central idea is novel, the first-stage evidence supports discrete diffusion as a viable alternative to AR, and the authors are honest about limitations. A serious referee could help them turn this into a solid contribution. The fixes are straightforward: add error bars or confidence intervals, match the FM baseline architecture more carefully, and evaluate on a held-out timbre set that is disjoint from any fine-tuning or validation use.

For peer review: yes, send it out. The idea deserves referee time, but the evaluation needs to be strengthened before the OOD claims are accepted.

Referee Report

4 major / 6 minor

Summary. The paper presents DDSynth-RL, a two-stage framework for Dexed FM synthesizer inversion. In the first stage, synthesizer parameters and MIDI tokens are discretized and modeled with a masked discrete diffusion model, which is compared against an autoregressive Transformer and a continuous flow-matching baseline. In the second stage, the supervised discrete diffusion model is fine-tuned with a GRPO-style objective using rewards computed from audio rendered by the non-differentiable Dexed VST. The authors report that discrete diffusion is competitive with or better than the baselines after supervised training, and that GRPO fine-tuning improves out-of-domain audio matching metrics on the NSynth dataset. The abstract and conclusion both emphasize the OOD improvement as the central empirical contribution.

Significance. Synthesizer inversion is a relevant MIR problem, and the paper addresses a real limitation: parameter-space losses do not necessarily align with rendered-audio similarity. The discrete-diffusion formulation over categorical synthesizer controls is a reasonable and potentially useful alternative to autoregressive and continuous-flow approaches, and the paper includes code, demos, and an AR-order ablation that help reproducibility. A strength is that the renderer is treated as a black box throughout, matching practical constraints. However, the significance of the empirical claims is currently limited by evaluation-design issues: the 'OOD' set appears to be the same NSynth validation set used during GRPO fine-tuning, the reward function includes the evaluation metrics, and all headline numbers are point estimates without error bars. If the authors can provide a genuinely held-out timbre evaluation and uncertainty quantification, the contribution would be solid and of interest to the sound-matching community.

major comments (4)

[Sections 4.1, 4.5, Table 1] The central claim that GRPO fine-tuning improves out-of-domain matching is not supported by the reported protocol. Section 4.1 states that NSynth train audio is used as target prompts for GRPO and that NSynth valid audio is used for validation. Section 4.5 then states that models are evaluated on the 'NSynth OOD validation set,' and Table 1 reports 'OOD NSynth' results. The evaluation split is therefore neither disjoint from the fine-tuning distribution nor unseen for checkpoint selection. The large OOD improvements after GRPO (e.g., wMFCC 10.04 to 5.96, MSS 4.41 to 3.11) could reflect optimization toward the reward on the in-distribution validation set rather than generalization to unseen timbres. Please report results on the untouched NSynth test split, or on a held-out timbre set that is disjoint from all training, validation, and checkpoint selection.
[Equation (7), Section 4.5, Table 1] The GRPO reward is a weighted sum of exactly the same metrics used for evaluation in Table 1: wMFCC, CLAP, CREPE, MSS, SOT, and RMS. Fine-tuning with a linear combination of these metrics and then reporting improvements on those metrics measures reward optimization, not independent audio-quality improvement. The claim in Section 4.6 that GRPO 'demonstrates the effectiveness of audio-domain rewards' would be much stronger if at least one held-out metric not used in the reward were reported, or if a control with a similarly shaped but unrelated reward were included (e.g., a random-reward or single-reward ablation showing that the improvement is not simply reward overfitting).
[Table 1] All results are point estimates averaged over 200 rendered examples, with no error bars, confidence intervals, or significance tests. Several claimed differences between DD and AR are small relative to what one would expect from sampling and rendering noise; for example, in-domain CLAP is 0.128 for AR versus 0.139 for DD, and OOD CLAP is 0.439 for AR versus 0.452 for DD. Without variance across seeds or a statistical test, the statements that DD is 'competitive' with AR, and that DD outperforms AR on OOD MFCC-related metrics, cannot be reliably assessed. Please report seed-level variance or bootstrap confidence intervals for the main comparisons.
[Section 4.2, Table 1] The FM baseline is not comparable to AR and DD in a controlled way: AR and DD share a 2D CNN encoder plus Transformer decoder, whereas FM uses an AST-style encoder and ApproxEquivTransformer. The conclusion that flow matching is substantially worse because of continuous-relaxation mismatch is therefore confounded with architecture and encoder differences. Either a controlled FM implementation using the same audio encoder and decoder depth, or an explicit argument that the Hayes et al. configuration is the appropriate reference, is needed before attributing the performance gap to the generative formulation.

minor comments (6)

[Section 4.1] The augmentation strategy is described as 'random perturbations to the synthesizer parameters,' but the specific perturbation ranges and which parameters are perturbed are not given. This matters because it determines the effective training distribution and the difficulty of the inversion task.
[Figure 3] The AR-order ablation reports averages over four seeds, but the number of random orders and the per-seed spread are not reported. Please add error bars or per-seed curves so that the claim that the manual order 'consistently' converges faster is visible.
[Equation (2), Section 3.2] The token weights w(i) are introduced as emphasizing parameters with stronger effects on rendered audio, but the paper never states how these weights are chosen. Please provide the values or the procedure used to set them.
[Section 4.3] The evaluation metrics are listed but several important settings are missing: the number of MFCC coefficients and DTW alignment constraints for wMFCC, the window sizes and scales for MSS, and the regularization used for SOT. Please provide these details or cite the exact implementation.
[Section 5] The observation that 'allowing the first-stage supervised model to overfit slightly does not necessarily produce the best objective audio metrics, but can lead to better subjective listening quality' is presented without a listening test or subjective evaluation. Please present evidence or mark it explicitly as an informal observation.
[Section 6 and Table 1] The conclusion says GRPO fine-tuning 'further improves OOD audio matching metrics,' but Table 1 shows that the CLAP+CREPE phase worsens several metrics relative to the multi-reward phase (e.g., OOD wMFCC 5.96 to 7.28). The claim should be qualified to say that the chosen reward combination improves the metrics it optimizes.

Circularity Check

1 steps flagged · score 6.0 of 10

The OOD GRPO improvement claim is substantially the optimized reward itself: Eq. (7) rewards the same audio metrics reported in Table 1, and evaluation is performed on NSynth valid, the split used for validation during fine-tuning.

fitted input called prediction [Section 3.4 (Eq. 7); Section 4.1; Section 4.5; Section 4.6 / Table 1]
"The distance terms D include wMFCC, CLAP embedding distance, CREPE embedding distance, multi-scale spectrogram distance, and spectral optimal transport distance, while RMS envelope cosine similarity is used as a similarity term: rk = −Σ_{j∈D} λj dj(âk,a) + λ_rms s_rms(âk,a). ... For GRPO fine-tuning, NSynth train audio serves as target prompts and NSynth valid audio is used for validation. ... GRPO fine-tuning further improves OOD audio matching metrics, demonstrating the effectiveness of audio-domain rewards for non-differentiable synthesizer inversion."

Eq. (7) is the objective being maximized by GRPO, and its distance terms dj are exactly the metrics reported in Table 1 for the OOD NSynth rows (wMFCC, MFCC13/MFCC40, MSS, SOT, RMS, CLAP; CREPE in the second RL phase). The policy is therefore fitted to increase the same numerical quantities that the paper then reports as evidence of improved OOD audio matching. The 'OOD' evaluation is on NSynth valid, which Section 4.1 states is used for validation during fine-tuning, so checkpoint selection also targets the reported split.

full rationale

The first-stage comparison — discrete diffusion versus autoregressive and flow-matching baselines — is a genuinely self-contained experimental contribution. The models are trained on the in-domain Dexed split and evaluated on held-out Dexed and NSynth splits under the same protocol; no circular reduction is present there. The AR order ablation is also an independent empirical finding. Self-citations in the paper (e.g., [28] for discrete diffusion in symbolic music) are background context and are not load-bearing for the central claim. The only significant circularity is the second-stage GRPO claim: the reward in Eq. (7) is a weighted sum of the same audio-distance metrics reported in Table 1, and the 'OOD' evaluation set overlaps with the NSynth validation split used during GRPO fine-tuning. Consequently, the reported improvement of DD-GRPO over Stage 1 on OOD metrics is partly guaranteed by the training objective, and the paper's strongest claim about out-of-domain generalization is not independently established. The absence of error bars over 200 examples compounds this, but that is a statistical-reporting concern rather than a circularity concern. Overall, the paper contains substantial independent content, but one central prediction reduces by construction to its own optimization target, giving a partial circularity score of 6.

Assumptions & free parameters 4 free parameters · 5 assumptions · 0 invented entities

The paper's central claims rest on hand-chosen token and reward weights, an unvalidated discretization of continuous controls, and the assumption that audio metrics capture perceptual similarity. No new physical or conceptual entities are introduced.

free parameters (4)

Token loss weights w(i) = not reported
In Eq. 2, w(i) weights the cross-entropy loss per parameter token to emphasize controls with stronger audio effects; the values are hand-chosen and not specified, yet they shape the supervised model.
Reward weights λ_j and λ_rms = not reported
In Eq. 7, the GRPO reward is a weighted sum of audio distances (wMFCC, CLAP, CREPE, MSS, SOT) plus RMS similarity; the weights are not given and are central to the fine-tuning behavior.
GRPO sampling hyperparameters (K, β, top-k, min-probability, epsilon) = K=8, β=0.1; others not reported
Section 4.5 gives K and β, but the stochastic best-of decoding parameters (top-k, minimum probability, epsilon-greedy) are not specified, yet they determine the explored trajectories.
Local smoothing kernel width for target distributions = not reported
Section 3.1 says locally smoothed target distributions are used for MIDI tokens and ordered quantized controls, but the smoothing width is not given.

assumptions (5)

domain assumption Dexed is treated as a non-differentiable black-box renderer, so audio-domain supervision must go through rewards rather than gradients.
Section 1 and 3.4 assume the synthesizer is non-differentiable, motivating GRPO; if a differentiable renderer were available, simpler loss backpropagation would be possible.
ad hoc to paper Continuous controls can be discretized into 25 bins without losing the information needed for audio matching.
Section 3.1 fixes 25 bins for quantized controls; the choice is not justified and the central results depend on this quantization fidelity.
domain assumption The audio metrics (wMFCC, CLAP, CREPE, MSS, SOT, RMS) measure perceptual audio similarity.
Section 4.3 adopts these metrics as evaluation and reward signals, but no human listening test is provided to validate them for this task.
domain assumption The masked discrete diffusion loss and entropy-based one-token-at-a-time decoding yield valid synthesizer parameter sequences.
Sections 3.2 and 3.3 assume the learned confidence-based decoding produces coherent parameter sets; no analysis of decoding failures is given.
standard math GRPO with K=8 candidates and β=0.1 provides a stable policy gradient update for this black-box objective.
Section 3.4 relies on the GRPO estimator from DeepSeek-Math [32] as a standard RL method; the specific hyperparameters are adopted without sensitivity analysis.

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Cite this review

Pith. "Pith review of DDSynth-RL: Audio Synthesizer Inversion via Discrete Diffusion with Reinforcement Learning." pith.science (2026). https://pith.science/paper/JIS2CUTP

@misc{pith2026260803032,
  author       = {Pith},
  title        = {Pith review of: DDSynth-RL: Audio Synthesizer Inversion via Discrete Diffusion with Reinforcement Learning},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/JIS2CUTP}},
  note         = {Machine review of arXiv:2608.03032}
}

read the original abstract

Synthesizer inversion is challenging for two main reasons: 1) Distinct parameter configurations can produce perceptually similar sounds. 2) Parameter-space losses often fail to reflect rendered audio similarity, while the synthesizer being a non-differentiable black box prevents simple audio-domain supervision. To address the one-to-many mapping induced by the first challenge, we formulate synthesizer inversion as conditional generation over discrete synthesizer parameters and use masked discrete diffusion as the generator. This treatment additionally avoids the fixed-order assumption of autoregressive models and the continuous-relaxation mismatch of flow matching when modeling categorical synthesizer controls. To address the second challenge, we further fine-tune the model with GRPO-style audio-domain rewards computed from rendered outputs. Experiments on Dexed show that, after supervised training, the discrete diffusion model is competitive with autoregressive and flow-matching baselines, and reward-based fine-tuning further improves out-of-domain audio matching performance. Code and demos are available at: https://github.com/DDSynth-RL/DDSynthRL.

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Works this paper leans on

47 extracted references · 43 canonical work pages

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2) Parameter-space losses often fail to reflect rendered audio similarity, while the synthesizer be- ing a non-differentiable black box prevents simple audio- domain supervision

Distinct parameter configurations can produce perceptu- ally similar sounds. 2) Parameter-space losses often fail to reflect rendered audio similarity, while the synthesizer be- ing a non-differentiable black box prevents simple audio- domain supervision. To address the one-to-many mapping induced by the first challenge, we formulate synthesizer in- versi...

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DDSynth-RL: Audio Synthesizer Inversion via Discrete Diffusion with Reinforcement Learning

INTRODUCTION Sound synthesizers underpin a wide range of creative prac- tices including music production, film and game sound de- sign, and new media art. Their controls are closely tied to human auditory perception and creative practices. In music AI research, symbolic music modeling has long been a central topic, but “symbolic” usually refers to notes a...

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Such iterative search often strug- gles to scale to modern high-dimensional synthesizers and may not transfer readily across parameter spaces

BACKGROUND 2.1 Synthesizer Inversion Synthesizer inversion, also known as parameter estima- tion or sound matching, was initially formulated as black- box optimization using genetic, evolutionary, or particle- swarm methods [8–11]. Such iterative search often strug- gles to scale to modern high-dimensional synthesizers and may not transfer readily across ...

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METHOD We use a two-stage framework for Dexed synthesizer in- version. First, we formulate inversion as conditional gen- eration over discrete synthesizer and MIDI tokens, and train a masked discrete diffusion model with supervised token-level objectives. Second, we fine-tune the super- vised model with GRPO using rewards computed from rendered audio. 3.1...

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EXPERIMENTS 4.1 Datasets We divide the experimental data into in-domain and out-of- domain (OOD) subsets. For the in-domain data, we use the Dexed parameter-audio dataset curated by Le Vaillant and Dutoit for SPINV AE-2 [36], and follow their augmenta- tion strategy by applying random perturbations to the syn- thesizer parameters and rendering each preset...

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DISCUSSION Although the autoregressive model can achieve perfor- mance close to discrete diffusion on the relatively fixed Dexed synthesizer setting, our ablation experiments show that its performance is highly sensitive to the generation order of parameter tokens. This heuristic design may not transfer reliably to more complex synthesizers, where the num...

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Experimental results show that discrete diffusion substantially outperforms Flow Matching while avoiding the need to predefine a parameter generation order

CONCLUSION In this paper, we study discrete diffusion modeling for au- dio synthesizer inversion and introduce GRPO fine-tuning with audio-domain rewards from a black-box synthesizer renderer. Experimental results show that discrete diffusion substantially outperforms Flow Matching while avoiding the need to predefine a parameter generation order. It also...

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AI USAGE STA TEMENT ChatGPT was used to assist with implementing the training code and writing and revising this paper

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ACKNOWLEDGEMENTS I thank Ziyuan Zhao and Lejun Min for insightful technical discussions, Liwei Lin for assistance with the paper’s fig- ures, and my advisor, Mark Nicholas Grimshaw-Aagaard, for his continued support and intellectual guidance
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Pith tools

Reviewed August 8, 2026 · model on record in the stance chip above.

[1] [1]

2) Parameter-space losses often fail to reflect rendered audio similarity, while the synthesizer be- ing a non-differentiable black box prevents simple audio- domain supervision

Distinct parameter configurations can produce perceptu- ally similar sounds. 2) Parameter-space losses often fail to reflect rendered audio similarity, while the synthesizer be- ing a non-differentiable black box prevents simple audio- domain supervision. To address the one-to-many mapping induced by the first challenge, we formulate synthesizer in- versi...

work page

[2] [2]

DDSynth-RL: Audio Synthesizer Inversion via Discrete Diffusion with Reinforcement Learning

INTRODUCTION Sound synthesizers underpin a wide range of creative prac- tices including music production, film and game sound de- sign, and new media art. Their controls are closely tied to human auditory perception and creative practices. In music AI research, symbolic music modeling has long been a central topic, but “symbolic” usually refers to notes a...

work page Pith review arXiv 2026

[3] [3]

Such iterative search often strug- gles to scale to modern high-dimensional synthesizers and may not transfer readily across parameter spaces

BACKGROUND 2.1 Synthesizer Inversion Synthesizer inversion, also known as parameter estima- tion or sound matching, was initially formulated as black- box optimization using genetic, evolutionary, or particle- swarm methods [8–11]. Such iterative search often strug- gles to scale to modern high-dimensional synthesizers and may not transfer readily across ...

work page

[4] [4]

METHOD We use a two-stage framework for Dexed synthesizer in- version. First, we formulate inversion as conditional gen- eration over discrete synthesizer and MIDI tokens, and train a masked discrete diffusion model with supervised token-level objectives. Second, we fine-tune the super- vised model with GRPO using rewards computed from rendered audio. 3.1...

work page

[5] [5]

EXPERIMENTS 4.1 Datasets We divide the experimental data into in-domain and out-of- domain (OOD) subsets. For the in-domain data, we use the Dexed parameter-audio dataset curated by Le Vaillant and Dutoit for SPINV AE-2 [36], and follow their augmenta- tion strategy by applying random perturbations to the syn- thesizer parameters and rendering each preset...

work page arXiv

[6] [6]

DISCUSSION Although the autoregressive model can achieve perfor- mance close to discrete diffusion on the relatively fixed Dexed synthesizer setting, our ablation experiments show that its performance is highly sensitive to the generation order of parameter tokens. This heuristic design may not transfer reliably to more complex synthesizers, where the num...

work page

[7] [7]

Experimental results show that discrete diffusion substantially outperforms Flow Matching while avoiding the need to predefine a parameter generation order

CONCLUSION In this paper, we study discrete diffusion modeling for au- dio synthesizer inversion and introduce GRPO fine-tuning with audio-domain rewards from a black-box synthesizer renderer. Experimental results show that discrete diffusion substantially outperforms Flow Matching while avoiding the need to predefine a parameter generation order. It also...

work page

[8] [8]

AI USAGE STA TEMENT ChatGPT was used to assist with implementing the training code and writing and revising this paper

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