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section6/section6.tex

@@ -184,14 +184,14 @@ Weng earns \$12 per hour for babysitting. Yesterday, she babysat for 50 minutes.
 
 
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-This demonstration teaches the model a tool-use pattern. The model learns to reason about the task, call an external tool when needed, read the returned observation, and continue the execution process. We refer to the response returned by the external tool as an ``observation''. The agent uses this observation to update its reasoning and complete the task.
+This demonstration teaches the model a tool-use pattern. The model learns to reason about the task, invoke an external tool when necessary, read the returned observation\footnote{We refer to the response returned by an external tool as an ``observation''.}, and continue the execution process.
 
 
 % 3. SFT for Tool Use
 % 3. SFT for Tool Use
 \subsubsubsection{Supervised Fine-Tuning}
 \subsubsubsection{Supervised Fine-Tuning}
 To make tool use more reliable, we can further train LLMs on tool-use trajectories with supervised fine-tuning. In this setting, each training example contains a user task, available tool descriptions, and a demonstrated tool-use process. The model learns when to invoke a tool, which tool to select, how to construct valid arguments, and how to use the returned observation. For example, we can construct a training example as follows:
 To make tool use more reliable, we can further train LLMs on tool-use trajectories with supervised fine-tuning. In this setting, each training example contains a user task, available tool descriptions, and a demonstrated tool-use process. The model learns when to invoke a tool, which tool to select, how to construct valid arguments, and how to use the returned observation. For example, we can construct a training example as follows:
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-    $\mathbf{x} =$ Task: ``Weng earns \$12 per hour and worked for 50 minutes. Save the result into the database.''; Tools: calculator(expression), update\_record(json) \newline
-    $\mathbf{y} =$ Thought: compute the payment; Action: calculator(12 x 50 / 60); Observation: 10; Action: update\_record({``earning'': 10}); Observation: success; Answer: Weng earned \$10.
+    $\mathbf{x} =$ Task: Weng earns \$12 ... Tools: calculator(expression), ... , update\_record(json) \newline
+    $\mathbf{y} =$ Thought: compute the payment; Action: calculator(12 x 50 / 60); ...; Answer: Weng earned \$10.
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