arxiv arXiv cs.LG · 99d ago · src: 100d ago · research

EnvRL: Leveraging Environment Dynamics in Agentic RL

from English

EnvRL introduces a framework that enhances agentic reinforcement learning by incorporating environment dynamics through state prediction and inverse dynamics objectives. When trained with GRPO, EnvRL improves success rates of Qwen-2.5-1.5B-Instruct from 72.8% to 77.4% on ALFWorld and from 56.8% to 67.0% on WebShop.

Importance 3/3 New feature vs. leaders New harness with differentiators arXiv cs.LG Alibaba (Qwen) AI agents Reasoning models Training methods

Read original

arxiv arXiv cs.CL · 99d ago · 1 view

EnvRL: Leveraging Environment Dynamics in Agentic RL

EnvRL introduces a framework that enhances agentic reinforcement learning by incorporating environment dynamics through state prediction and inverse dynamics objectives. It achieves significant gains in success rates on long-horizon benchmarks, improving Qwen-2.5-1.5B-Instruct performance from 72.8% to 77.4% on ALFWorld and from 56.8% to 67.0% on WebShop when trained with GRPO.

arxiv arXiv cs.CL · 99d ago · 6 views

LLM-Designed Training Environment for RL with Multi-Agent Reasoning

The LLM-as-Environment-Engineer framework uses LLMs to automatically redesign training environments in reinforcement learning by analyzing failure trajectories and contextual data. On the MAPF-FrozenLake testbed, it outperforms larger proprietary LLMs and fixed-environment baselines, with Qwen3-4B achieving the strongest aggregate performance. Analysis shows that failure evidence and preserved working configurations are key, and the current RL checkpoint performs better than the base model as an environment engineer.

arxiv arXiv cs.CL · just now Live

VHD-Play generates agentic RL environments from solved mechanisms

VHD-Play is a pipeline that generates diverse agentic reinforcement learning environments by sampling and solving mathematical models before rendering their decision processes as stateful tools. This approach ensures that executable dynamics and trajectory-scoring references are inherited directly from the solved model, addressing the misalignment issues common in existing generation pipelines.

arxiv arXiv cs.CL · 1h ago · 1 view Live

SkillGym internalizes human skills into LLMs for real-world problem solving

Researchers introduce SkillGym, a framework that transforms human-written agent skills into executable, verifiable training environments for large language model agents. The system utilizes a skill-to-task pipeline to instantiate concrete tasks and verify outcomes with code-based checkers.

arxiv arXiv cs.CL · 15d ago · 25 views

ExecCritic uses role-specific RL to improve coding agents via test-guided repair

Researchers introduce ExecCritic, a framework that combines a test-verify-revise scaffold with role-specific reinforcement learning to enhance coding agents. The system separates test construction from source-code repair, using a Test agent to generate repository-native tests and a Repair agent to revise code based on execution feedback.