ObsSpec: Accelerating Agentic Reinforcement Learning with Observation Speculation
We introduce ObsSpec, an observation speculation pipeline for agentic RL that uses a small world model to predict tool outputs while the environment executes, allowing the policy to begin its next generation early.
ProofPaper ↗
Key points
- Tool-using agents alternate between model generation and tool execution, waiting for each tool call to complete before starting the next generation.
- This sequential execution is costly during agentic reinforcement learning (RL), which requires large batches of multi-turn rollouts.
- Across controlled experiments, we characterize the regimes in which ObsSpec reduces agent latency under shared policy resources.
- Finally, because ObsSpec overlaps policy generation with tool execution, it is architecturally compatible with existing techniques for accelerating RL.
Sources (1)
- [1]ObsSpec: Accelerating Agentic Reinforcement Learning with Observation SpeculationarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 11, 12:40 AM
We introduce ObsSpec, an observation speculation pipeline for agentic RL that uses a small world model to predict tool outputs while the environment executes, allowing the policy to begin its next generation early.
Tool-using agents alternate between model generation and tool execution, waiting for each tool call to complete before starting the next generation.
Extractive summary: sentences quoted from the sources.
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