---
title: Any-to-Any Training Strategy
url: https://www.emergentmind.com/topics/any-to-any-training-strategy
type: topic
---

# Any-to-Any Training Strategy

Any-to-Any Training Strategy denotes a family of training regimes in which a model, or a compositional set of model components, is prepared to map arbitrary valid inputs to arbitrary valid outputs inside a structured domain, rather than only a fixed source–target pair. Across the literature, the phrase is used for transfer from pure-strategy opponents to arbitrary opponent mixtures in multi-agent reinforcement learning, for translation between arbitrary feature modalities in heterogeneous collaborative perception, for voice conversion between arbitrary speakers, for interleaved multimodal understanding and generation across text, image, audio, video, document, code, and 3D, and for transfer between humanoid embodiments [2009.14180] [2605.17907] [2010.14150] [2510.09061] [2603.05075] [2311.18775] [2309.05519] [2412.01169] [2606.16408] [2512.15420] [2510.13721] [2605.23733]. The common objective is not uniform architecture, but a shared operational property: a single training recipe should support a large family of source conditions and target conditions, often with zero-shot or parameter-efficient deployment to configurations not explicitly optimized as one-off tasks.

## 1. Domain-specific meanings of “any-to-any”

The term is domain-relative rather than universal. In Q-Mixing, the problem is a two-player Markov game in which the opponent has a finite set of pure strategies, and the goal is to construct a policy that performs well against any mixture \(w \in \Delta^K\) using only Q-functions trained against pure opponents [2009.14180]. In UniTrans, the objective is to realize mappings \(A_{s\to t}: F_{m_s}\rightarrow F_{m_t}\) for arbitrary source–target modality pairs in collaborative perception, including unseen modalities at inference [2605.17907]. In FragmentVC, MediumVC, and O\_O-VC, “any-to-any” means conversion from arbitrary source speakers to arbitrary target speakers, including unseen speakers and, in O\_O-VC, new languages [2010.14150] [2110.02500] [2510.09061].

In multimodal foundation models, the term expands further. UniM defines any-to-any interleaved multimodal learning as accepting sequences that interleave placeholders for text, image, audio, video, document, code, and 3D, and producing outputs that may themselves be arbitrarily interleaved across the same modalities [2603.05075]. CoDi-2, NExT-GPT, OmniFlow, MUNI, FlowBind, and NExT-OMNI each operationalize this idea differently—through language-centric continuous conditioning, modality signal tokens, multi-modal rectified flows, routed shared latents, bidirectional ODE flows, or discrete flow matching—but all aim at arbitrary cross-modal input–output mappings under one training umbrella [2311.18775] [2309.05519] [2412.01169] [2606.16408] [2512.15420] [2510.13721].

A plausible implication is that “any-to-any” is best understood as a closure property over a task family. The admissible family may be a simplex over known opponents, a set of known or unseen feature modalities, a speaker space, a subset lattice over modalities, or a morphology class of robots. The phrase therefore indicates breadth of transferable conditioning, not a single canonical loss or architecture.

## 2. Recurrent architectural patterns

A first recurrent pattern is factorization through reusable intermediate structure. Q-Mixing learns separate best-response Q-functions \(Q^{(i)}\) against pure opponents and approximates the mixed-opponent value by
\[
Q_{\text{mix}}(s,a;\mathbf{w}) \approx \sum_{i=1}^{K} w_i Q^{(i)}(s,a),
\]
thereby converting a combinatorial target family into a linear composition over trained components [2009.14180]. UniTrans uses an analogous but parametric mechanism: a Translator Parameter Bank stores reusable expert parameters, and a mapping-specific translator is instantiated by
\[
\Theta_{s\to t} = \theta^{(0)} + \sum_{k=1}^{K} \alpha_k \theta^{(k)},
\]
with the coefficients predicted from a modality mapping descriptor in a modality-intrinsic latent space [2605.17907].

A second pattern is the construction of shared latent spaces that mediate arbitrary conditional routes. FlowBind defines a learnable shared latent \(z^* = H_\phi(z_S)\) and modality-specific invertible ODE flows that can be integrated backward from a source modality into \(z^*\) and forward into a target modality [2512.15420]. MUNI replaces deterministic bridging with a shared stochastic latent, modality-specific decoders, and a learned flow-based prior; its routed objective is explicitly designed to make subset latents predictive for missing modalities while keeping the shared latent minimal [2606.16408]. UniTrans also belongs to this family, but uses a modality-intrinsic latent \(Z\) built from channel statistics and Gram descriptors rather than a generative shared prior [2605.17907].

A third pattern is modular alignment around a large pretrained core. NExT-GPT keeps ImageBind, Vicuna-7B, and the diffusion decoders frozen, and trains only an input projection, output projections, and LoRA adapters—131M trainable parameters, roughly 1% of the total system capacity [2309.05519]. CoDi-2 similarly aligns modalities with language on both the encoding and generation sides, with the MLLM emitting continuous conditioning features that drive pretrained diffusion decoders [2311.18775]. NExT-OMNI uses a single Qwen2.5-7B-based backbone and lightweight modality heads over unified discrete and continuous codebook embeddings, replacing decoupled pipelines with a bidirectionally fused encoder that supports understanding, generation, and retrieval in one representation space [2510.13721].

A fourth pattern is deliberate asymmetry. FragmentVC aligns source phonetic structure and target spectral structure through a two-stage curriculum, while MediumVC converts \(X_i \rightarrow Y_i \rightarrow X_i\) through a specific-speaker intermediate, and O\_O-VC trains on synthetic source–target pairs with identical linguistic content and aligned durations [2010.14150] [2110.02500] [2510.09061]. These strategies reject symmetric autoencoding as the default any-to-any recipe and instead use asymmetric factorization to force content–speaker separation or direct source–target alignment.

## 3. Optimization regimes and objective design

Despite the conceptual similarity, the optimization layer differs sharply by domain. In reinforcement learning, Q-Mixing uses standard off-policy Q-learning, optionally with Double DQN, and can augment deployment with an opponent classifier trained in parallel via cross-entropy on replay-buffer data [2009.14180]. In collaborative perception, UniTrans uses a two-stage objective: InfoNCE and modality classification for modality-intrinsic representation pretraining, followed by feature distillation, downstream task loss, routing consistency, and router regularization during translator expert learning [2605.17907].

Voice-conversion methods are dominated by reconstruction losses, but the training semantics differ. FragmentVC trains end-to-end with reconstruction loss only,
\[
L_{\text{rec}} = \|\hat{S}-S\|_1,
\]
using a frozen Wav2Vec 2.0 encoder, a target spectrogram bank, and a two-stage curriculum that decays the probability that the source utterance appears among target utterances from 1.0 to 0.0 between steps 50k and 150k [2010.14150]. MediumVC also uses L1 reconstruction, but places it inside a two-stage asymmetrical pipeline \(X_i \rightarrow Y_i \rightarrow X_i\) built around synthetic specific-speaker speeches as intermedium features [2110.02500]. O\_O-VC adds a CVAE+flow+GAN stack with mel L1, KL divergence, adversarial, and feature-matching terms, and splits training into synthetic conversion followed by transcript-free real reconstruction fine-tuning [2510.09061].

Multimodal any-to-any systems exhibit the widest objective diversity. UniM and UniMA combine autoregressive cross-entropy over interleaved sequences with auxiliary structure-aware losses for modality-type coverage, placeholder counts, structural validity, and cross-modal alignment, while the agentic baseline additionally supervises intermediate reasoning artifacts such as TCDC, plan strings, tool lists, and final reports [2603.05075]. CoDi-2 combines text causal loss, feature regression to aligned encoder targets, and diffusion denoising loss that backpropagates through the MLLM’s continuous outputs [2311.18775]. OmniFlow uses multi-modal rectified-flow regression on task-encoding paths through a noise-level cube, with modality-specific target velocities \(v_i = x_i^0 - x_i^1\), plus a generalized multi-modal classifier-free guidance mechanism at inference [2412.01169]. FlowBind reduces training to a single flow-matching objective over straight paths between per-modality latents and a learnable shared anchor, whereas MUNI uses a routed multimodal variational objective with self-detached posteriors and prior gating to full and leave-one-out subsets [2512.15420] [2606.16408]. NExT-OMNI trains discrete flow matching by expected cross-entropy against the posterior \(p_{1|t}(x_1\mid x_t)\), augmented by image and audio reconstruction regularizers balanced by GradNorm [2510.13721].

Robotics introduces yet another regime. Any2Any first performs kinematic alignment between source and target humanoids and then optimizes only LoRA-based PEFT parameters in dynamics-sensitive modules under PPO, while freezing the source specialist backbone and preserving reward design and domain randomization from pretraining [2605.23733]. This suggests that any-to-any training can be expressed not only as generalized conditional generation, but also as a structured restriction on which parameters may move during transfer.

## 4. Deployment, zero-shot behavior, and transfer mechanics

A central distinction is whether any-to-any capability is realized at inference without further optimization, or through a lightweight adaptation phase. Q-Mixing is explicitly zero-shot with respect to new opponent mixtures: once the pure-opponent Q-functions are learned, deployment to any \(w \in \Delta^K\) requires only computing the mixed value and acting greedily or via softmax, with no further RL training [2009.14180]. UniTrans likewise instantiates translators on the fly for arbitrary source–target modality pairs by computing modality codes, a mapping descriptor, routing coefficients, and then a single parameter-combined translator, with no retraining or fine-tuning needed for new modality pairs [2605.17907].

In multimodal generative systems, the dominant deployment pattern is conditional decoding over a unified token or latent stream. NExT-GPT routes LLM-emitted modality signal tokens through modality-specific output projections into frozen diffusion condition encoders, so the same model can answer in text or activate image, audio, and video generation on demand [2309.05519]. CoDi-2 and OmniFlow perform synchronized multimodal generation by keeping modalities aligned in a shared conditioning space or along shared rectified-flow time coordinates, which allows intertwined outputs such as image–audio or video–audio to be generated in parallel [2311.18775] [2412.01169]. FlowBind composes backward ODE integration from arbitrary observed modalities into a shared latent with forward integration into arbitrary target modalities, and multi-source conditioning is handled by averaging shared-latent estimates from each source [2512.15420]. NExT-OMNI replaces autoregressive token-by-token decoding with parallel discrete flow updates over the full sequence, and its Dynamic Length Generation Strategy adjusts response length through EOS confidence while preserving blockwise parallel refinement [2510.13721].

Other systems are not zero-shot in this strict sense, but still qualify as any-to-any because they minimize target-side optimization. Any2Any transfers a pretrained whole-body tracking specialist to a new humanoid embodiment with kinematic alignment plus PEFT on selected dynamics-sensitive modules, using only a small amount of target data and compute [2605.23733]. O\_O-VC generalizes to unseen speakers and new languages after a two-phase training schedule, but still relies on a training-time synthetic-pair construction and a subsequent real-data fine-tuning phase [2510.09061]. This suggests that “any-to-any” should not be conflated with “single frozen universal model”; in part of the literature it instead means universal transferability under sharply constrained adaptation.

## 5. Empirical record across domains

The empirical literature reports gains in coverage, structural validity, or transfer efficiency rather than a single universal benchmark.

| Setting | Representative finding | Paper |
|---|---|---|
| Multi-agent RL | In grid-world soccer, Q-Mixing achieved broadly stronger coverage than BR(Uniform); in Gathering, BR\((\pi_{-i}^0)\) achieved \(227.93 \pm 21.01\) vs \(207.98 \pm 15.92\) for BR\((\sigma)\) | [2009.14180] |
| Heterogeneous collaborative perception | On OPV2V-H, UniTrans reached \(0.716\) AP@0.5 / \(0.605\) AP@0.7; on DAIR-V2X, \(0.553/0.421\) | [2605.17907] |
| Any-to-any voice conversion | FragmentVC reported SV Accuracy \(94.8\) on seen-to-seen and \(92.5\) on unseen-to-unseen; naturalness MOS was \(3.26 \pm 0.12\) | [2010.14150] |
| Synthetic-pair voice conversion | O\_O-VC reported a \(16.35\%\) relative reduction in word error rate and a \(5.91\%\) improvement in speaker cosine similarity | [2510.09061] |
| Interleaved multimodal benchmark | UniM contains 31,026 instances; UniMA reaches StS \(\approx 50\)–\(71\%\), LeS \(\approx 62\)–\(84\%\), SQCS \(\approx 60\%\) with \(\tau=100\%\), and ICS approaches \(\approx 70\%\) | [2603.05075] |
| Cross-embodiment humanoid transfer | Any2Any transferred Sonic from Unitree G1 to LimX Oli and LimX Luna using only \(1\%\) of the compute and data required for full training | [2605.23733] |

Additional evidence comes from multimodal generation. NExT-GPT reports best zero-shot text-to-video FID \(13.04\) and CLIPSIM \(0.3085\) on MSR-VTT, while remaining competitive on text-to-image and text-to-audio benchmarks [2309.05519]. OmniFlow reports FID \(13.40\) and CLIP \(31.54\) on MSCOCO-30k for text-to-image, and FAD \(1.75\) with CLAP \(0.183\) on AudioCaps for text-to-audio [2412.01169]. FlowBind reports competitive quality while requiring up to \(6\times\) fewer parameters and training \(10\times\) faster than prior methods, and MUNI reports its largest gains on unconditional coherence rather than only conditional generation [2512.15420] [2606.16408]. NExT-OMNI reports strong averages on OmniBench, WorldSense, and AV-Odyssey, leads OpenING multi-turn interaction among open unified models, and improves average Top-5 retrieval accuracy to \(32.9\) on InfoSeek, OVEN, FashionIQ, and CIRR [2510.13721].

## 6. Limitations, controversies, and open directions

The main limitation is that “any” is always bounded. Q-Mixing generalizes to any mixture over a known set of pure opponents; mixtures involving unseen opponent types lie outside the convex hull of the training basis [2009.14180]. UniTrans explicitly notes that if a new modality lies far outside the training latent manifold, mapping estimates may be unreliable [2605.17907]. FlowBind depends on strong frozen modality-specific encoders and decoders and does not optimize an explicit likelihood over its shared latent; this trades tractable joint modeling for efficiency [2512.15420]. MUNI warns that if the prior-weighting hyperparameters are too large, unconditional coherence can dominate at the expense of conditional routes, while disabling self-detach causes modality-private details to leak into the shared latent [2606.16408].

A second limitation is structural instability. Router collapse is a concern in UniTrans and motivates routing-consistency and load-balancing losses [2605.17907]. UniM documents common structural lapses such as incorrect placeholder counts, missing modalities, or incoherent interleavings, and makes these part of the benchmark itself through StS, LeS, and ICS [2603.05075]. NExT-OMNI identifies a granularity trade-off: without reconstruction regularizers, discrete flow matching can bias the shared representation toward semantics and degrade fine-grained generation, whereas too many sub-codebooks increase generation difficulty [2510.13721].

A third limitation is that unified training does not eliminate deployment bottlenecks inherited from downstream generators. CoDi-2 and NExT-GPT both depend on pretrained diffusion decoders, so overall fidelity and latency remain bounded by those modules [2311.18775] [2309.05519]. OmniFlow notes that some text-generation results still lag specialist captioners, in part because noisy weakly labeled training texts differ from benchmark caption distributions [2412.01169]. FragmentVC depends on vocoder quality, and O\_O-VC depends on the fidelity and diversity of the multispeaker TTS used to synthesize aligned training pairs [2010.14150] [2510.09061].

The literature also contains a substantive privacy controversy. In asynchronous voice anonymization, targeted training that maps all utterances to a designated speaker can improve unlinkability, but the designated identity is an actual speaker, creating privacy and impersonation risks. The any-to-any alternative replaces the fixed target with a pseudo-speaker defined as the batch mean of anonymized embeddings and optimizes a batch mean loss, thereby improving unlinkability without designating a real person as the anonymization sink [2508.15565]. The same work shows that unlinkability remains fragile under adaptive perturbation-removal attacks and degrades out of domain, indicating that any-to-any objectives do not automatically confer adversarial robustness.

Future directions in the cited literature are consistent: non-linear or observation-aware mixing beyond linear composition, richer Bayesian or recurrent belief updates, stronger structure-aware decoding constraints, route-aware latent objectives, scaling to continuous or open-ended modality spaces, improved robustness to unseen domains and adaptive attacks, and increasingly explicit reuse of pretrained specialists rather than monolithic retraining [2009.14180] [2605.17907] [2603.05075] [2606.16408] [2508.15565] [2605.23733]. This suggests that the mature form of any-to-any training is likely to remain modular: aligned latent spaces, reusable experts, structured routing, and targeted adaptation, rather than a single universally optimal backbone.

Source: https://www.emergentmind.com/topics/any-to-any-training-strategy