CacheRL:Multi-Turn Tool-Calling Agents via Cached Rollouts and Hybrid Reward
We present CacheRL, a system for training small agent foundation models that achieves 92 percent process accuracy on multi-step tool-calling tasks, approaching GPT-5's 94 percent while requiring 100 times less compute. Our approach addresses three challenges in practical agent training: transferring tool-calling knowledge from large models at scale, enabling reinforcement learning without costly live tool execution, and learning robustly from noisy cached environments. CacheRL introduces three key innovations. First, a hybrid thinking trajectory pipeline augments agent trajectories with LLM-generated reasoning traces, producing training examples that teach models not only what tools to ca...
| Authors | Md Amirul Islam, Sumiran Thakur, Huancheng Chen et al. |
|---|---|
| Primary category | cs.CL |
| Published | 12 June 2026 |
| Last updated at source | 12 June 2026 |
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