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Life sciences · Preprint
arXiv · August 11, 2026
Posted before peer review. The findings may change or fail to hold.
This is an unrefereed engineering preprint introducing TideRL, a system for scheduling and resource allocation in multi-turn reinforcement learning training. It reports improvements in computational throughput and efficiency metrics over baseline systems, but lacks peer review, independent validation, and formal statistical evidence.
Systems engineering empirical evaluation. Multi-turn agentic RL workloads with variable task duration and external environment interaction. Intervention: TideRL system with Continuous Task Batching, Resource-Aware Ref-Actor Pipelining, and Elastic Resource Scaling. Compared with: Synchronous and asynchronous RL training baselines.
TideRL improves RL training goodput by up to 5.6× over synchronous baselines TideRL improves RL training goodput over 33% over asynchronous baselines KV cache hit rate improved by 1.58×
Safety was not reported in the material analysed. Check the source before drawing any conclusion about harm.
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This is a systems engineering preprint describing a novel RL training infrastructure optimization; it has not undergone peer review and reports computational performance metrics rather than clinical or validated scientific outcomes.
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Reinforcement learning (RL) for large language models is moving toward multi-turn agentic workloads, where rollout tasks repeatedly pause for external environments, resume with growing contexts, and finish at highly variable times. In this setting, RL training goodput, measured by training throughput, matters more than raw GPU occupancy: GPU waiting and repeated prefill recomputation are pure overhead. We present TideRL, a readiness-aware elastic RL system with Continuous Task Batching, Resource-Aware Ref-Actor Pipelining, and Elastic Resource Scaling. CTB preserves useful rollout state, $\textrm{RA}^2\textrm{P}$ selects between decoupled streaming and colocated aggregation from the ready backlog and arrival interval, and ERS moves ranks between rollout and training using the same readiness signals. Across text-only and multi-modal agentic workloads, TideRL improves RL training goodput by up to 5.6$\times$ over synchronous baselines and over 33% over asynchronous baselines, while reaching similar task performance. It also improves KV cache hit rate by 1.58$\times$, reduces per-step training time by up to 44.3%, and cuts total waiting time by up to 77.6%.
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