该框架提出解耦的跨任务信用分配方法，在 ALFWorld、WebShop 和 ScienceWorld 三个基准上取得了最佳结果，优于提示方法和 RL 基线。
SkillRise is an end-to-end reinforcement learning framework for cross-task skill learning. A single policy plays an ordered sequence of related tasks, alternating between solving the current task with an evolving skill document and curating that document from the resulting trajectory before moving to the next task. Evaluated on ALFWorld, WebShop, and ScienceWorld.
SkillRise (a) constructs progressively challenging task sequences from the same task family, (b) rolls out a single policy that alternates Solve and Curate so the skill document evolves and transfers across tasks, and (c) uses decoupled cross-task credit assignment with group-relative advantages computed only within the same role and position.
pip install -r requirements.txt # verl is vendored in verl/, no separate install
Environment backends:
Edit examples//env.sh (set SKILLRISE_MODEL_PATH, data paths, WANDB_API_KEY),
then:
bash examples/skillrise_alfworld/skillrise_alfworld_qwen3_4b.sh # SkillRise
bash examples/skillrise_alfworld/grpo_alfworld_qwen3_4b.sh # GRPO baseline
Same layout for skillrise_webshop/ and skillrise_sciworld/. Task sequences
are bundled under data/groups/.
SkillRise achieves the best overall results across ALFWorld, WebShop, and ScienceWorld, outperforming prompting-based methods and RL baselines.
Apache License 2.0 (derivative of verl / verl-agent). See LICENSE and
NOTICE.
This project builds on veRL, verl-agent, BEACON, ALFWorld, WebShop, and ScienceWorld. We thank the authors of those projects.
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