TL;DR
SituatedThinker enhances large language models by grounding their reasoning in real-world contexts through situated thinking, combining internal knowledge with external information via reinforcement learning to improve performance on diverse reasoning tasks.
Contribution
Introduces SituatedThinker, a framework that grounds LLM reasoning in real-world contexts using reinforcement learning and external interfaces, surpassing knowledge limitations.
Findings
Significant improvements on multi-hop question-answering and mathematical reasoning benchmarks.
Strong performance on unseen tasks like KBQA, TableQA, and text-based games.
Demonstrates generalizable real-world grounded reasoning capability.
Abstract
Recent advances in large language models (LLMs) demonstrate their impressive reasoning capabilities. However, the reasoning confined to internal parametric space limits LLMs' access to real-time information and understanding of the physical world. To overcome this constraint, we introduce SituatedThinker, a novel framework that enables LLMs to ground their reasoning in real-world contexts through situated thinking, which adaptively combines both internal knowledge and external information with predefined interfaces. By utilizing reinforcement learning, SituatedThinker incentivizes deliberate reasoning with the real world to acquire information and feedback, allowing LLMs to surpass their knowledge boundaries and enhance reasoning. Experimental results demonstrate significant performance improvements on multi-hop question-answering and mathematical reasoning benchmarks. Furthermore,…
Peer Reviews
Decision·Submitted to ICLR 2026
1. The problem of grounding the LLM in real-world situations is important to explore. 2. On multi-hop question-answering and mathematical reasoning, the model significantly outperforms baseline methods.
1. I am still unclear about the distinction between "Situated Thinking" and standard tool learning. The "Interfaces" defined in the paper, which include the description, the specified input format, and outputs, do not seem fundamentally different from the LLM agent function calling used in the community today. The only difference is the representation, such as text-based tags versus JSON Schema, which makes the contribution incremental. 2. One of the contributions is that the paper claims that
- This paper is well-motivated and well-written. It’s good to show how general tool-use capabilities can be injected into smaller language models via RL to boost reasoning. - The results on MATH and knowledge-based reasoning datasets seem strong.
- Interface design: he authors customized a new interface design for external tool calls. The specifications look highly similar to the Model Context Protocol (MCP). Why not just use MCP, since it’s more universal and enables a wider range of tool use? - It’s hard to see what actually works. From Table 8, we can see that the tool calls hardly affect model performance on one of the major claimed domains—mathematical reasoning—while on knowledge-based tasks, it’s unclear how retrieval-only method
- The single-turn formulation of situated thinking paradigm is, to my knowledge, novel. This formulation should greatly simplify the RL training pipeline as compared to more complex multi-turn tool-use enhanced reasoning models. - The resulting models seem to generalize to unseen domains not explicitly covered in the training data. - The paper is generally well-written and easy to understand.
- I find some of the statements about novelty over-claimed: - It’s unclear how the claim that situated thinking, which is described as “a new paradigm that allows LLMs to adaptively engage with external environments” in line 91, is different from several LLM-based agents that use retrieval tools, like web-search, e.g., [1,2,3,4], or even baselines ReSearch and Search-R1 (beyond number of tools used). - The exact way tool results are incorporated (directly within a single-turn long CoT) may
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