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AgentMercury introduces a scalable framework for synthesizing verifiable environments from business scenarios, enabling reinforcement learning agents to improve performance on enterprise and out-of-domain benchmarks.
A new research paper introduces AgentMercury, a scalable framework for synthesizing executable environments from business scenarios, which improves agent training performance on various benchmarks.
Envs-FORGE is a prompting policy for synthesizing training environments for reinforcement learning agents, converting verifier rewards into per-seed actions to improve performance on benchmarks like SWE-bench and Terminal-Bench.
EnvFactory automates the creation of executable tool environments and natural multi-turn trajectories for training LLMs with agentic reinforcement learning, achieving superior performance on benchmarks like BFCLv3 and MCP-Atlas with fewer environments than prior work.
This paper proposes EvoEnv, a method where language models construct verifiable Python environments for self-improvement through reinforcement learning, achieving a 3.3% relative gain on Qwen3-4B-Thinking.
EnvScaler is an automated framework for scaling tool-interactive environments for LLM agents through programmatic synthesis, creating 191 diverse environments and 7K scenarios to improve agent performance on multi-turn, multi-tool interactions.
Agent-World introduces a self-evolving training framework for general agent intelligence that autonomously discovers real-world environments and tasks via the Model Context Protocol, enabling continuous learning. Agent-World-8B and 14B models outperform strong proprietary models across 23 challenging agent benchmarks.