Heddle: A Distributed Orchestration System for Agentic RL Rollout

Dimensions overall score 7.0

• Heddle achieves up to 2.5x higher end-to-end rollout throughput compared to state-of-the-art baselines.result 90%
  Evidencepartial

  achieving up to 2.5$\times$ higher end-to-end rollout throughput compared to state-of-the-art baselines.

  Implicationpartial

  Directly stated in the abstract with a clear numeric result.

  Verificationpartial

  partial

• Rollout generation is the dominant bottleneck in agentic RL, consuming over 80% of the entire training time.technical 90%
  Evidencepartial

  analysis [17, 33] identifies rollout generation as the dominant bottleneck, consuming over 80% of the entire training time.

  Implicationpartial

  Directly stated in the analysis excerpt with a citation to empirical analysis.

  Verificationpartial

  partial

• Frequent tool calls in agentic RL induce a long-tailed distribution of trajectory generation times, creating a straggler effect.technical 85%
  Evidencepartial

  frequent tool calls induce long-tailed trajectory generation that bottlenecks rollouts.

  Implicationpartial

  Explicitly stated in the abstract and analysis as the core problem being addressed.

  Verificationpartial

  partial

• Heddle's trajectory-level scheduling uses runtime prediction and progressive priority to minimize cumulative queueing delays.method 85%
  Evidencepartial

  trajectory-level scheduling using runtime prediction and progressive priority to minimize cumulative queueing

  Implicationpartial

  Directly described as a core mechanism in the abstract and detailed in the analysis.

  Verificationpartial

  partial

• Heddle employs trajectory-aware placement via presorted dynamic programming and opportunistic migration to minimize interference overhead.method 85%
  Evidencepartial

  trajectory-aware placement via presorted dynamic programming and opportunistic migration during idle tool call intervals to minimize interference

  Implicationpartial

  Explicitly listed as a core mechanism in the abstract and detailed in the system overview.

  Verificationpartial

  partial

• Heddle's trajectory-adaptive resource manager dynamically tunes model parallelism to accelerate per-token time for long-tail trajectories while maintaining high throughput for short ones.method 85%
  Evidencepartial

  trajectory-adaptive resource manager that dynamically tunes model parallelism to accelerate the per-token time of long-tail trajectories while maintaining high throughput for short trajectories.

  Implicationpartial

  Explicitly listed as a core mechanism in the abstract and detailed in the analysis.

  Verificationpartial

  partial

• Heddle masks migration overhead by transmitting data asynchronously during tool-call intervals, keeping the critical execution path unblocked.method 80%
  Evidencepartial

  Heddle masks this migration overhead by transmitting data asynchronously during tool-call intervals, keeping the critical execution path unblocked.

  Implicationpartial

  Directly stated in the analysis excerpt describing the opportunistic migration mechanism.

  Verificationpartial

  partial

• In agentic RL, high sampling temperatures are often employed to encourage exploration, inherently inducing high output variance in trajectory length.technical 80%
  Evidencepartial

  To encourage exploration, high sampling temperatures are often employed, inherently inducing high output variance.

  Implicationpartial

  Directly stated in the analysis as a cause of the long-tail distribution.

  Verificationpartial

  partial

Competitive landscape

Heddle is a distributed system that optimizes agentic reinforcement learning rollouts by intelligently scheduling and managing tool calls, achieving up to 2.5x higher throughput.