{"slug": "efficient-reinforcement-learning-for-long-horizon-tool-use-agentic-tasks", "title": "Efficient Reinforcement Learning for Long-Horizon Tool-Use Agentic Tasks", "summary": "Researchers introduced SINKFLEX-RL, a modular training system for reinforcement learning in long-horizon tool-use agentic tasks, which integrates a Gymnasium-compatible environment wrapper, VERL-style rollout dataflow, group-relative policy optimization, and a sink-aware FlexAttention path. In a Tau2Bench retail run, validation reward (mean@1) improved from 0.25 to 0.44, and the optimized attention path reduced peak VRAM from 28.06GB to 22.52GB at 4096 tokens (a 19.7% reduction), enabling an 8192-token configuration to run with 25.53GB where the eager baseline ran out of memory.", "body_md": "arXiv:2608.10357v1 Announce Type: new\nAbstract: Long-horizon tool-using agents must reason over user goals, domain policies, tool calls, simulator state, and delayed verifiable rewards. Reinforcement learning (RL) is a natural fit for this setting, but multi-turn on-policy rollouts create long contexts, while model-specific attention layers may require custom masks and learned sink normalization. We present SINKFLEX-RL, a modular training system for RL in dual-control tool-use environments. The system combines a Gymnasium-compatible environment wrapper, a VERL-style rollout dataflow, group-relative policy optimization without a separate value model, and a sink-aware FlexAttention path designed to preserve model-specific sink scaling under causal and sliding-window masks. In a preliminary Tau2Bench retail run, validation reward (mean@1) rises from 0.25 early in training to $0.44$ later in the observed training window, while training-score and trajectory-reward proxies also trend upward. In a fixed-configuration memory benchmark, the optimized attention path reduces peak VRAM from 28.06GB to 22.52GB at 4096 tokens, a $19.7\\%$ reduction, and runs the measured 8192-token configuration using $25.53$~GB where the eager baseline runs out of memory. These results illustrate the value of integrating environment interfaces, RL dataflow, and attention-kernel design for memory-feasible long-horizon agent training.", "url": "https://wpnews.pro/news/efficient-reinforcement-learning-for-long-horizon-tool-use-agentic-tasks", "canonical_source": "https://www.machinebrief.com/news/efficient-reinforcement-learning-for-long-horizon-tool-use-a-frkv", "published_at": "2026-08-12 04:00:00+00:00", "updated_at": "2026-08-12 04:15:12.838680+00:00", "lang": "en", "topics": ["artificial-intelligence", "machine-learning", "ai-infrastructure", "ai-research"], "entities": ["SINKFLEX-RL", "Gymnasium", "VERL", "FlexAttention", "Tau2Bench"], "alternates": {"html": "https://wpnews.pro/news/efficient-reinforcement-learning-for-long-horizon-tool-use-agentic-tasks", "markdown": "https://wpnews.pro/news/efficient-reinforcement-learning-for-long-horizon-tool-use-agentic-tasks.md", "text": "https://wpnews.pro/news/efficient-reinforcement-learning-for-long-horizon-tool-use-agentic-tasks.txt", "jsonld": "https://wpnews.pro/news/efficient-reinforcement-learning-for-long-horizon-tool-use-agentic-tasks.jsonld"}}