// The agent is its own best speculator //
Agents spend a large share of wall-clock time waiting on tool results. Speculation hides that latency by predicting and pre-executing the next call, but external draft models and cached traces are modeling a different policy, so they miss too often to help.
New research from UC Santa Barbara and LinkedIn unifies both roles in one model. It runs in agent mode to solve the task and in speculator mode to predict its next tool call from a partial trajectory, fully reusing the prefix KV cache.
Joint agent-speculator reinforcement learning derives speculation targets from the agent's own rollouts and alternates updates between the two modes, which keeps dual-mode training from degrading task success.
Next tool-call Hit
@1 rises from 44.1 to 61.2 for Qwen3-4B and from 48.9 to 66.3 for Qwen3.5-4B, with agent task success preserved.
Paper:
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