yolay/SPEAR-ALFWorld-DrBoT-GiGPO-1.5B

Hugging Face
TEXT GENERATIONPricing:Input $0.04 / Cached $0.008 / Output $0.08Concurrent Unit Cost:1Model Size:1.5BQuant:BF16Context Size:32kTool Calling:SupportedPublished:Sep 27, 2025License:apache-2.0Architecture:Transformer Open Weights Featherless Exclusive Warm

yolay/SPEAR-ALFWorld-DrBoT-GiGPO-1.5B is a 1.5 billion parameter model developed by Yulei Qin and collaborators, based on the Qwen2.5-1.5B-Instruct architecture. It implements the SPEAR (Self-imitation with Progressive Exploration for Agentic Reinforcement Learning) framework, designed for training agentic LLMs on long-horizon, sparse-reward tasks. This model excels at balancing exploration and exploitation through a curriculum-based self-imitation learning approach, achieving significant performance gains on benchmarks like ALFWorld and WebShop compared to baseline methods.

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SPEAR-ALFWorld-DrBoT-GiGPO-1.5B Overview

This model, developed by Yulei Qin and collaborators, is a 1.5 billion parameter agentic LLM based on the Qwen2.5-1.5B-Instruct architecture, fine-tuned using the SPEAR (Self-imitation with Progressive Exploration for Agentic Reinforcement Learning) framework. SPEAR is specifically designed to enhance the training of LLM agents on complex, long-horizon tasks with sparse rewards by balancing exploration and exploitation.

Key Capabilities

  • Curriculum-based Self-Imitation Learning: Employs an adaptive curriculum that first uses auxiliary tool-use rewards for broad skill exploration, then strengthens self-imitation to leverage successful past trajectories.
  • Stable Training: Achieves a stable balance between exploration and exploitation, mitigating over-uncertainty in decision-making under shifting external feedback.
  • Enhanced Performance on Agentic Tasks: Demonstrates significant improvements over vanilla GRPO, Dr.BoT, and GiGPO methods on benchmarks like ALFWorld and WebShop.
    • On ALFWorld, SPEAR improved GRPO by +16.1 and Dr.BoT(GiGPO) by +2.6, reaching 93.2%.
    • On WebShop, SPEAR improved GRPO by +20.7 and Dr.BoT(GiGPO) by +8.1, reaching 81.1%.

Good for

  • Developing LLM agents for environments requiring long-horizon planning.
  • Tasks with sparse reward signals where effective exploration and exploitation are critical.
  • Research and application in agentic reinforcement learning and tool-use scenarios for LLMs.

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