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ARXIV:2606.03077 · AGENTS · SUBMITTED 03 JUN · 20:45 UTC · FRESHNESS FRESH
ARXIV:2606.03077AGENTSSUBMITTED 03 JUN · 20:45 UTCFRESHNESS FRESHKaiwen Chen · Xin Tan · Jingzong Li · Hong Xu · arXiv
Libra optimizes resource management for agentic RL by dynamically allocating GPUs and using a novel scheduler to handle long-tailed, non-stationary workloads, improving throughput and convergence.
Opportunity summary
Pain Libra optimizes resource management for agentic RL by dynamically allocating GPUs and using a novel scheduler to handle long-tailed, non-stationary workloads, improving throughput and convergence.
Evidence 0 refs | 3 sources | 50% coverage
Blocker Evidence unverified
Libra optimizes resource management for agentic RL by dynamically allocating GPUs and using a novel scheduler to handle long-tailed, non-stationary workloads, improving throughput and convergence. In agentic RL, the rollout stage generates trajectories while…
Reinforcement learning (RL) has become a standard post-training paradigm for large language models (LLMs), extending beyond preference alignment to complex reasoning and multi-turn agentic behaviors. In agentic RL, the rollout stage generates trajectories while…
ScienceToStartup currently rates this 6.0/10 on the public viability pass. It leverages an elastic hybrid pool to enable lightweight, non-blocking worker reallocation between stages.
Agents moved forward this cycle; last verified June 2026. Public score 6.0/10.
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Libra optimizes resource management for agentic RL by dynamically allocating GPUs and using a novel scheduler to handle long-tailed, non-stationary workloads, improving throughput and convergence.
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Paper Pack
10.48550/arXiv.2606.03077Libra optimizes resource management for agentic RL by dynamically allocating GPUs and using a novel scheduler to handle long-tailed, non-stationary workloads, improving throughput and convergence.
Abstract
Reinforcement learning (RL) has become a standard post-training paradigm for large language models (LLMs), extending beyond preference alignment to complex reasoning and multi-turn agentic behaviors. In agentic RL, the rollout stage generates trajectories while invoking tools, producing long-tailed and non-stationary workloads that challenge conventional resource-management assumptions. Three fundamental challenges arise. First, due to the long-tail distribution, a small fraction of trajectories dominates rollout makespan. Second, rollout and training exhibit strong asymmetry in compute patterns, memory demands, and sensitivity to sequence length. Third, as the RL policy evolves, the trajectory-length distribution drifts over time, rendering any static resource split progressively suboptimal. We present Libra, which introduces two core mechanisms. The first is a periodic global resource planner that jointly optimizes GPU allocation across rollout and training clusters. It leverages an elastic hybrid pool to enable lightweight, non-blocking worker reallocation between stages. The second is a causality-driven multi-level feedback queue (C-MLFQ) scheduler, which routes requests to heterogeneous rollout buckets based on causal signals derived from tool-return outcomes, rather than relying on fragile length predictions. Evaluated on 48 A800 GPUs, Libra achieves up to 3.0$\times$ higher throughput and converges up to 2.5$\times$ faster in reward compared to the baselines.
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unverified0 refs; 3 sources; 50% coverage.
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PROBLEM
Libra optimizes resource management for agentic RL by dynamically allocating GPUs and using a novel scheduler to handle long-tailed, non-stationary workloads, improving throughput and convergence. In agentic RL, the rollout stage generates trajectories while invoking tools, prod...
METHOD
Reinforcement learning (RL) has become a standard post-training paradigm for large language models (LLMs), extending beyond preference alignment to complex reasoning and multi-turn agentic behaviors. In agentic RL, the rollout stage generates trajectories while invoking tools, p...
RESULT
ScienceToStartup currently rates this 6.0/10 on the public viability pass. It leverages an elastic hybrid pool to enable lightweight, non-blocking worker reallocation between stages.
WHY NOW
Agents moved forward this cycle; last verified June 2026. Public score 6.0/10.
{"file name": "input.pdf", "number of pages": 18, "author": "Kaiwen Chen; Xin Tan; Jingzong Li; Hong Xu", "title": "Libra: Efficient Resource Management for Agentic RL Post-Training"
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Libra optimizes resource management for agentic RL by dynamically allocating GPUs and using a novel scheduler to handle long-tailed, non-stationary workloads, improving throughput and convergence.
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6.0/10 public viability
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