61 lines
3.2 KiB
Markdown
61 lines
3.2 KiB
Markdown
---
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license: apache-2.0
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language:
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- en
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pipeline_tag: text-generation
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library_name: transformers
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tags:
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- agent
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- reasoning
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- tool-use
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- simulative-planning
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base_model: Qwen/Qwen3-30B-A3B-Thinking-2507
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---
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# SR²AM-v1.0-30B
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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**.
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**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.
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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.
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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).
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## Key Features
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- **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.
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- **SFT + RL training**: supervised learning on data encoding the self-regulated planning structure, followed by reinforcement learning (GRPO) for task success.
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- **Agentic tool use**: web search (SerpAPI), web browsing with LLM summarization, and stateless Python code execution (SandboxFusion).
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- **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.
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- **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.
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## Quick Start
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See the [GitHub repository](https://github.com/sailing-lab/sr2am) for setup and inference instructions.
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## Main Results
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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).
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## Citation
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```bibtex
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@article{deng2026sr2am,
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title={Efficient Agentic Reasoning Through Self-Regulated Simulative Planning},
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author={Deng, Mingkai and Hou, Jinyu and Neves, Lara Sá and
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Pimpalkhute, Varad and Killian, Taylor W. and
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Liu, Zhengzhong and Xing, Eric P.},
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journal={arXiv preprint arXiv:2605.22138},
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year={2026}
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}
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```
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## License
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Released under the [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0).
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