SFT#Before reinforcement learning, we perform a supervised fine-tuning warmup to produce well-formed tool calls, follow the retrieval subagent prompt format and learn strong behavior priors such as parallel tool calling and query decomposition. We generate SFT trajectories by running the full agent loop with large models such as Kimi K2.5 as the inference backend. Each rollout produces a complete trajectory: the initial prompt, the model's reasoning and tool calls at each turn, the tool results, and the final document set.
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