ECHO-2 cuts RL training costs 33-36% by distributing cheap rollout generation while keeping expensive policy learning centralized.
Reinforcement learning (RL) is a critical stage in post-training large language models (LLMs), involving repeated interaction between rollout generation, reward evaluation, and centralized learning. Distributing rollout execution offers opportunities to leverage more cost-efficient inference resources, but introduces challenges in wide-area coordination and policy dissemination. We present ECHO-2, a distributed RL framework for post-training with remote inference workers and non-negligible dissemination latency. ECHO-2 combines centralized learning with distributed rollouts and treats bounded policy staleness as a user-controlled parameter, enabling rollout generation, dissemination, and training to overlap. We introduce an overlap-based capacity model that relates training time, dissemination latency, and rollout throughput, yielding a practical provisioning rule for sustaining learner utilization. To mitigate dissemination bottlenecks and lower cost, ECHO-2 employs peer-assisted pipelined broadcast and cost-aware activation of heterogeneous workers. Experiments on GRPO post-training of 4B and 8B models under real wide-area bandwidth regimes show that ECHO-2 significantly improves cost efficiency while preserving RL reward comparable to strong baselines.
ECHO-2 achieves 33-36% cost reduction by moving rollout generation to cheaper distributed resources instead of premium GPU clusters.
Policy staleness is treated as a control parameter, allowing rollouts from policies up to S steps old to reduce synchronization overhead.
The system balances rollout generation and policy learning through an overlap condition that keeps the learner continuously busy without idle time.
Wide-area distributed systems introduce latency and heterogeneous throughput that can create training inefficiencies if not carefully managed.
The approach assumes modern RL objectives like GRPO are robust to moderate policy lag, which may not apply to all RL settings.
Guobin Shen, Chenxiao Zhao +3
Training stability remains a central challenge in reinforcement learning (RL) for large language models (LLMs). Policy staleness, asynchronous training, and mismatches between training and inference e...
Zhixia Zhang, Zixuan Huang +8
We introduce Heterogeneous Agent Collaborative Reinforcement Learning (HACRL), a new learning paradigm that addresses the inefficiencies of isolated on-policy optimization. HACRL enables collaborative...
Fengkai Yang, Zherui Chen +11
Reinforcement Learning from Verifier Rewards (RLVR) has emerged as a widely used approach for post-training large language models on reasoning tasks, with group-based methods such as GRPO and its vari...
Jiyuan Wang, Chunyu Lin +9
Leveraging the priors of 2D diffusion models for 3D editing has emerged as a promising paradigm. However, maintaining multi-view consistency in edited results remains challenging, and the extreme scar...
Yanqi Dai, Yuxiang Ji +4
Reinforcement Learning with Verifiable Rewards (RLVR) offers a robust mechanism for enhancing mathematical reasoning in large models. However, we identify a systematic lack of emphasis on more challen...
