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[ICML 2026] ETS: Energy-Guided Test-Time Scaling for Training-Free RL Alignment

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# ETS: Energy-Guided Test-Time Scaling for Training-Free RL Alignment

[![arXiv](https://img.shields.io/badge/arXiv-2601.21484-b31b1b.svg)](https://arxiv.org/abs/2601.21484)
[![Zhihu](https://img.shields.io/badge/Zhihu-ETS-blue)](https://zhuanlan.zhihu.com/p/2001982062979215914)
[![X](https://img.shields.io/badge/X-Article-black)](https://x.com/sheriyuo/status/2042072816712085577)

## News

- [2026.05.01] ETS is accepted as a poster at ICML 2026.
- [2026.01.29] We have uploaded ETS paper to [arXiv](https://arxiv.org/abs/2601.21484), and open-sourced ETS on [Github](https://github.com/sheriyuo/ETS)!

### New features in this repo

- [2026.05.15] Support vllm for ETS and AIME24 avg@32 evaluation for all methods.

## Introduction

We introduce **ETS (Energy-Guided Test-Time Scaling)**, a *training-free* inference method that samples directly from the **optimal RL policy** under a unified **Masked Language Modeling (MLM)** framework that covers both:
- **Autoregressive Models (ARMs)**
- **Diffusion Language Models (DLMs)**

**Core idea:**  
For RL objective, the optimal policy admits a closed-form structure. ETS leverages this to construct an *optimal transition kernel* that factorizes into:
- a **reference transition** given by a base model $p_{\mathrm{ref}}$, and  
- an **energy term** that is a conditional expectation of exponentiated rewards.

![](https://raw.githubusercontent.com/sheriyuo/ETS/main/main_fig.png)

## Setup
Run the following script to setup environment.

```bash
git clone https://github.com/sheriyuo/ETS.git
cd ETS
pip install -e .
```

## Evaluation
### Hyperparameters you will tune

ETS compute is dominated by three hyperparameters:
- $M$: number of candidates per guidance step
- $K$: number of Monte Carlo estimation, $K=3$ works best in most cases
- $I$: number of guidance steps

For evaluating **autoregressive models (Qwen)**, the ETS compute parameters map to:
- $M$: `m_candidates` 
- $K$: `k_monte_carlo`
- $I$ is **implicit** . It is determined by the total decoding length and block granularity:
  - `max_length` = total generation length $d_x$
  - `block_size` = block length $B$
  - so $I = \lceil \mathrm{max\_length} / \mathrm{block\_size} \rceil$

For evaluating **diffusion language models (LLaDA)**, the mapping is explicit:
- $I$: `guide_steps`
- $M$: `num_candidates`
- $K$: `monte_carlo_num`

We evaluate in a pass@1 setting on:

- Math/Reasoning: MATH500, GSM8K

- Coding: HumanEval

- STEM: GPQA (Diamond)

### Autoregressive model

Evaluate ETS with transformers:

```bash
cd qwen
bash eval.sh
```

Evaluate ETS with vllm:

```bash
cd qwen_vllm
bash eval.sh
```

### Diffusion Language model

```bash
cd llada
bash eval.sh
```

### Evaluation for AIME24 (lm_eval)

1. Download [BytedTsinghua-SIA/AIME-2024](https://huggingface.co/datasets/BytedTsinghua-SIA/AIME-2024) to a local directory and update the data path in `aime24/aime24.yaml` to point to your local dataset.
2. Replace the existing `utils.py` and `aime24.yaml` in `lm_eval/tasks/aime/`. For example:

```bash
rm -rf /usr/local/miniconda3/lib/python3.10/site-packages/lm_eval/tasks/aime/aime24.yaml
rm -rf /usr/local/miniconda3/lib/python3.10/site-packages/lm_eval/tasks/aime/utils.py
cp aime24/aime24.yaml /usr/local/miniconda3/lib/python3.10/site-packages/lm_eval/tasks/aime/aime24.yaml
cp aime24/utils.py /usr/local/miniconda3/lib/python3.10/site-packages/lm_eval/tasks/aime/utils.py
```

3. Execute `eval_aime.sh`.

## Citation

```bibtex
@inproceedings{li2026ets,
  title={ETS: Energy-Guided Test-Time Scaling for Training-Free RL Alignment},
  author={Li, Xiuyu and Zhang, Jinkai and Yi, Mingyang and Li, Yu and Wang, Longqiang and Wang, Yue and Fan, Ju},
  booktitle={Forty-third International Conference on Machine Learning},
  year={2026}
}
```

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