mixinputs 0.1.1

Mixture of Inputs Patch for vLLM

🎨 Mixture of Inputs (MoI) 🎨

Text Generation Beyond Discrete Token Sampling (arXiv)

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What is Mixture of Inputs?

Mixture of Inputs (MoI) is a simple, training-free technique to improve autoregressive text generation.

In standard LLM inference, the model samples a token and discards the full distribution. MoI keeps both—it mixes the sampled token with the original distribution to retain more information.

MoI uses Bayesian estimation to compute mixing weights:

• Prior: the output distribution

• Observation: the sampled token

• Posterior: a smooth input replacing the one-hot vector

This lets the model use a richer representation state $\boldsymbol{h}_t$ as inputs throughout generation, improving coherence, reasoning, and code synthesis.

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Installation

Requires vllm >= 0.8.5.

pip install vllm
pip install mixinputs
mixinputs setup

Quick Start

To activate MoI, set the MIXINPUTS_BETA environment variable:

export MIXINPUTS_BETA=1.0

Then run your usual vLLM-based generation script. That's it!

CLI Utilities

The patch is installed via injection to usercustomize.py by adding import mixinputs at the head.

We provide command-line tools to patch or unpatch your environment:

# Enable MoI
mixinputs setup

# Disable MoI
mixinputs cleanup

You can also enable it via adding import mixinputs in your script before you import vllm. The mixinputs will not activate without the environment variable MIXINPUTS_BETA.

Configuration Options

Variable	Description	Default
MIXINPUTS_BETA	Controls the strength of the mixture input signal	1.0

Recommended range: 0.5 to 2.0.

Tune based on task/model—lower values emphasize the distribution, higher values keep more of the sample.

Make sure enforce_eager=True in your LLM initialization.

Set MIXINPUTS_BETA=-100 to activate direct mixture, i.e., mixing with solely output distributions.

Examples

We have included 2 example usages with AIME and Count Down 4, after executing the bash you should see 59-60 Acc for CountDown4 (Nemotron-Super-49B) and ~80 Acc for AIME (QwQ-32B).

For external evaluation packages, simply set export MIXINPUTS_BETA=<BETA_VALUE> and use vllm as the backbone, will activate MoI.

Additionally, MoI also work with server mode, we provide an example script.

Evaluations

Model	Method	Input Info.	AIME (%)	CountDown4 (%)	GPQA-D (%)	LiveCodeBench (pass@1)	Avg (%)
QwQ-32B	Standard	Output Token	77.78	79.25	58.08	76.32	72.86
	Direct Mixture	Output Dist.	72.00	66.88	51.52	53.42	60.96
	MoI	Token + Dist.	80.00	80.01	60.10	76.51	74.15
	Gain vs. Standard		+2.22	+0.76	+2.02	+0.19	+1.29
Nemotron-Super-49B	Standard	Output Token	54.89	56.93	60.60	39.92	53.09
	Direct Mixture	Output Dist.	60.00	51.72	60.10	16.04	46.97
	MoI	Token + Dist.	57.11	59.53	64.65	40.50	55.45
	Gain vs. Standard		+2.22	+2.60	+4.05	+0.58	+2.36
Gemma-3-27B	Standard	Output Token	25.56	56.51	46.97	31.31	40.09
	Direct Mixture	Output Dist.	26.44	55.47	51.52	31.99	41.36
	MoI	Token + Dist.	26.89	59.38	47.47	32.87	41.65
	Gain vs. Standard		+1.33	+2.87	+0.50	+1.56	+1.56
DAPO-Qwen-32B	Standard	Output Token	64.67	72.03	42.42	54.01	58.28
	Direct Mixture	Output Dist.	62.67	67.19	37.88	23.87	47.90
	MoI	Token + Dist.	64.44	78.75	42.93	55.18	60.33
	Gain vs. Standard		–0.23	+6.72	+0.51	+1.17	+2.05

Questions?

If you have any questions related to the code or the paper, feel free to reach out to us at [e-mail].

Citation

If you find our paper and code useful, please cite us:

@article{zhuang2025textgen,
  title={Text Generation Beyond Discrete Token Sampling},
  author={Zhuang, Yufan and Liu, Liyuan and Singh, Chandan and Shang, Jingbo and Gao, Jianfeng},
  journal={arXiv preprint arXiv:2505.14827},
  year={2025}
}