---
license: apache-2.0
language:
- en
pipeline_tag: text-generation
library_name: transformers
tags:
- agent
- reasoning
- tool-use
- simulative-planning
base_model: Qwen/Qwen3-30B-A3B-Thinking-2507
---

# SR²AM-v1.0-30B

![SR²AM Illustration](model.png)

We argue that efficient agentic reasoning benefits from decomposing deliberation into three interacting systems: **reactive execution** (System I) for fine-grained reasoning and direct action; **simulative reasoning** (System II) that predicts consequences of proposed actions through a world model; and **self-regulation** (System III) that decides *when* and *how deeply* to plan through a learned **configurator**.

**SR²AM** (Self-Regulated Simulative Reasoning Agentic LLM) is our instantiation: the configurator and simulative planner are realized as distinct stages within an LLM's chain-of-thought reasoning, with the LLM itself serving as the world model in language space.

SR²AM-v1.0-30B achieves an overall Pass@1 of **71.3** across 11 benchmarks spanning math, science, tabular analysis, and web information seeking — competitive with systems at 685B–1T parameters, while consuming **25–95% fewer reasoning tokens** than comparably sized agentic LLMs.

More details: [project website](https://sailing-lab.github.io/sr2am-self-regulated-planning) | [paper](https://arxiv.org/abs/2605.22138) | [GitHub](https://github.com/sailing-lab/sr2am).

## Key Features

- **System I + II + III decomposition**: a configurator (System III) decides per-turn whether to plan, continue an existing plan, or act directly; a simulative planner (System II) constructs plans grounded in predicted future states; reactive execution (System I) handles fine-grained reasoning and tool use.
- **SFT + RL training**: supervised learning on data encoding the self-regulated planning structure, followed by reinforcement learning (GRPO) for task success.
- **Agentic tool use**: web search (SerpAPI), web browsing with LLM summarization, and stateless Python code execution (SandboxFusion).
- **Reasoning efficiency**: 5,518 reasoning tokens per trajectory on average — 51% fewer than MiroThinker-v1.5-30B and 95% fewer than ASearcher-Web-QWQ-v2 for comparable or better accuracy.
- **RL learns to plan further, not more often**: RL increases average planning horizon by 22.8% while planning frequency grows only 2.0 percentage points.

## Quick Start

See the [GitHub repository](https://github.com/sailing-lab/sr2am) for setup and inference instructions.

## Main Results

![Pass@1 vs. parameter size and reasoning-token count](main-results.png)

SR²AM-v1.0-30B sits above the size-vs-accuracy trendline in (a) and on the Pareto frontier of reasoning-token efficiency vs. accuracy among 30/32B models in (b). The full benchmark breakdown is in the [paper](https://arxiv.org/abs/2605.22138).

## Citation

```bibtex
@article{deng2026sr2am,
  title={Efficient Agentic Reasoning Through Self-Regulated Simulative Planning},
  author={Deng, Mingkai and Hou, Jinyu and Neves, Lara Sá and
          Pimpalkhute, Varad and Killian, Taylor W. and
          Liu, Zhengzhong and Xing, Eric P.},
  journal={arXiv preprint arXiv:2605.22138},
  year={2026}
}
```

## License

Released under the [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0).