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Life sciences · Preprint
arXiv · September 8, 2026
Posted before peer review. The findings may change or fail to hold.
This preprint proposes a feedback-enrichment strategy (FEEs) for training large language models as autonomous agents in long-horizon RL tasks, claiming improved performance on two computational benchmarks. The work is unpublished and offers no clinical application, real-world validation, or statistical rigor reporting.
Computational benchmarking study with pilot and large-scale experiments. Large language models of various scales (Qwen3 family); evaluated as autonomous agents in long-horizon reasoning tasks.. Intervention: Feedback-Enriched Environments (FEEs): environment-side adaptation via reformulated feedback design transitioning from action guidance to observation enrichment.. Compared with: Standard environment settings without feedback enrichment.
FEEs consistently yield performance improvements over standard settings on SciWorld and BFCL benchmarks Training with FEEs stabilizes training dynamics by reducing entropy volatility FEEs facilitate proactive state-space exploration in difficult tasks
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This is an unrefereed preprint describing a novel environment-design approach for training LLM agents via RL, with computational experiments on benchmarks but no peer-reviewed publication and no clinical or direct human outcomes.
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Large Language Models demonstrate remarkable proficiency in static reasoning, yet training them as autonomous agents through Reinforcement Learning (RL) for long-horizon tasks is often hindered by severe reward sparsity. While conventional \textit{agent-side warming} up via supervised fine-tuning (SFT) can alleviate this, it is frequently limited by data scarcity and constrained exploration. To address this, we propose a paradigm shift to \textit{environment-side adaptation} by constructing \textbf{F}eedback-\textbf{E}nriched \textbf{E}nvironments (\textbf{FEEs}). Through a pilot study, we establish a feedback design strategy that reformulates environments by transitioning from action guidance to observation enrichment during the later stages of both intra-episode exploration and inter-episode evolution. Large-scale experiments on SciWorld and BFCL benchmarks using various Qwen3 model scales and RL algorithms such as GRPO, GSPO, and DAPO demonstrate that FEEs consistently yield performance improvements over standard settings. Furthermore, our analysis reveals that training with FEEs \textbf{(1)} stabilizes training dynamics by reducing entropy volatility, \textbf{(2)} facilitates proactive state-space exploration in difficult tasks, \textbf{(3) }ensures the internalization of environmental guidance into policy weights rather than acting as a mere inference-time prior, and \textbf{(4) }identifies intra-group feedback consistency as a critical boundary for stable optimization.
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