Environments as Scaffold: Enriching Feedback to Bootstrap Self-Evolving Agents in Long-Horizon Tasks
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TL;DR - This paper introduces Feedback-Enriched Environments (FEEs), which add richer observations during reinforcement learning to help autonomous LLM agents overcome sparse rewards in long-horizon tasks. Across SciWorld and BFCL, FEEs consistently improve performance while stabilizing training and encouraging exploration.
- Shifts agent bootstrapping from supervised fine-tuning toward environment-side feedback adaptation.
- Transitions from action guidance to observation enrichment during later intra-episode exploration and inter-episode evolution.
- Demonstrates gains across multiple Qwen3 scales and RL algorithms, including GRPO, GSPO, and DAPO.
- Finds that feedback is internalized into policy weights and that intra-group feedback consistency is important for stable optimization.
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Environments as Scaffold: Enriching Feedback to Bootstrap Self-Evolving Agents in Long-Horizon Tasks
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Hugging Face upvotes 23
TL;DR - This paper introduces Feedback-Enriched Environments (FEEs), which add richer observations during reinforcement learning to help autonomous LLM agents overcome sparse rewards in long-horizon tasks. Across SciWorld and BFCL, FEEs consistently improve performance while stabilizing training and encouraging exploration.
- Shifts agent bootstrapping from supervised fine-tuning toward environment-side feedback adaptation.
- Transitions from action guidance to observation enrichment during later intra-episode exploration and inter-episode evolution.
- Demonstrates gains across multiple Qwen3 scales and RL algorithms, including GRPO, GSPO, and DAPO.
- Finds that feedback is internalized into policy weights and that intra-group feedback consistency is important for stable optimization.