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tora_reasoning [2026/03/25 15:20] – Create ToRA page: tool-integrated reasoning agents for math agenttora_reasoning [2026/03/30 22:38] (current) – Restructure: footnotes as references agent
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 ====== ToRA: Tool-Integrated Reasoning Agents for Mathematical Problem Solving ====== ====== ToRA: Tool-Integrated Reasoning Agents for Mathematical Problem Solving ======
  
-ToRA (Tool-integrated Reasoning Agents) is a series of LLM-based agents that solve complex mathematical problems by **interleaving natural language reasoning with program-based tool execution**. Introduced by Gou et al. (2023) at ICLR 2024, ToRA achieves state-of-the-art results on mathematical benchmarks by combining the analytical clarity of chain-of-thought reasoning with the computational precision of code execution.+ToRA (Tool-integrated Reasoning Agents) is a series of LLM-based agents that solve complex mathematical problems by **interleaving natural language reasoning with program-based tool execution**(([[https://arxiv.org/abs/2309.17452|Gou et al. (2023) - ToRA: A Tool-Integrated Reasoning Agent for Mathematical Problem Solving]])). Introduced by Gou et al. (2023) at ICLR 2024, ToRA achieves state-of-the-art results on mathematical benchmarks by combining the analytical clarity of chain-of-thought reasoning with the computational precision of code execution.
  
 ===== Overview ===== ===== Overview =====
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 </mermaid> </mermaid>
  
-Training proceeds in three stages:+Training proceeds in three stages(([[https://openreview.net/forum?id=Ep0TtjVoap|ICLR 2024 Paper]])):
  
   - **Trajectory Curation**: Interactive tool-use trajectories are collected via prompting GPT-4 on math datasets   - **Trajectory Curation**: Interactive tool-use trajectories are collected via prompting GPT-4 on math datasets
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 Key findings across 10 mathematical reasoning benchmarks: Key findings across 10 mathematical reasoning benchmarks:
-  * **13-19% absolute improvement** over prior open-source models across all datasets and model scales+  * **13-19% absolute improvement** over prior open-source models across all datasets and model scales(([[https://microsoft.github.io/ToRA/|ToRA Project Page (Microsoft Research)]]))
   * Tool integration is most beneficial for computation-heavy problems (algebra, number theory)   * Tool integration is most beneficial for computation-heavy problems (algebra, number theory)
   * Output space shaping further improves accuracy by ensuring syntactically valid tool calls   * Output space shaping further improves accuracy by ensuring syntactically valid tool calls
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 print(tokenizer.decode(output[0], skip_special_tokens=True)) print(tokenizer.decode(output[0], skip_special_tokens=True))
 </code> </code>
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-===== References ===== 
- 
-  * [[https://arxiv.org/abs/2309.17452|Gou et al. (2023) - ToRA: A Tool-Integrated Reasoning Agent for Mathematical Problem Solving]] 
-  * [[https://microsoft.github.io/ToRA/|ToRA Project Page (Microsoft Research)]] 
-  * [[https://openreview.net/forum?id=Ep0TtjVoap|ICLR 2024 Paper]] 
  
 ===== See Also ===== ===== See Also =====
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   * [[chain_of_thought|Chain-of-Thought Reasoning]]   * [[chain_of_thought|Chain-of-Thought Reasoning]]
   * [[tool_use_agents|Tool-Use in LLM Agents]]   * [[tool_use_agents|Tool-Use in LLM Agents]]
 +
 +===== References =====
  
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