@mdeng34: Frontier LLMs are converging on efficient, adaptive reasoning. Opus 4.7 lets the model decide how deeply to reason. GPT…
Summary
New research introduces SR²AM, a configurator that self-regulates when to use simulative reasoning, improving efficiency and performance in LLMs.
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Cached at: 05/23/26, 08:15 PM
Frontier LLMs are converging on efficient, adaptive reasoning. Opus 4.7 lets the model decide how deeply to reason. GPT-5.5 achieves strong results with fewer reasoning tokens.
We study a related but more structural question: what 𝗸𝗶𝗻𝗱 𝗼𝗳 𝗿𝗲𝗮𝘀𝗼𝗻𝗶𝗻𝗴 should we adapt?
Last year in SiRA (upper figure), we showed that simulative reasoning (System II), which uses a 𝘄𝗼𝗿𝗹𝗱 𝗺𝗼𝗱𝗲𝗹 to evaluate consequences of actions, yields up to 124% improvement over reactive baselines (System I), and that strong reasoning models (o1, o3-mini) fail as planners without this structure.
In our new paper SR²AM (lower figure), we add a learned 𝗰𝗼𝗻𝗳𝗶𝗴𝘂𝗿𝗮𝘁𝗼𝗿 (System III) that self-regulates when to simulate, how far ahead, and when to skip planning entirely.
Efficient reasoning is not just shorter reasoning: it is better allocation of simulation.
How does self-regulated simulative reasoning perform in practice?
SR²AM-v0.1-8B achieves results competitive with GPT-OSS (120B) and GLM-4.6 (355B).
SR²AM-v1.0-30B is competitive with DeepSeek-V3.2 (685B) and Kimi-K2.5 (1T) at 𝟮𝟲–𝟵𝟱% fewer reasoning tokens than comparable 30/32B agentic LLMs.
The key finding from RL training: the model learns to plan further ahead (+22.8% horizon) rather than more often (+2% frequency). Allocation, not compression.
This is a prototype using language-based world models. Stay tuned for our next steps on multimodal and physical world models.
The concept of a configurator, which decides when and how deeply to engage a reasoning process, is not specific to planning, but extensible to learning and adaptation going forward.
SR²AM: https://arxiv.org/abs/2605.22138 SiRA: https://arxiv.org/abs/2507.23773 Project: https://sailing-lab.github.io/sr2am-self-regulated-planning… Code: https://github.com/sailing-lab/sr2am…
SR²AM-v0.1-8B: https://huggingface.co/sailing-lab/SR2AM-v0.1-8B… SR²AM-v1.0-30B: https://huggingface.co/sailing-lab/SR2AM-v1.0-30B…
Joint work with @jinyuhou0, @larasnevess, @varad0309, @tw_killian, @waterluffy, @ericxing
Exactly! Thank you for the apt summary
Our work is grounded in reinforcement learning (Sutton & Barto), but propose new insights about the meaning and structure of decision making
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