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With external tools, large language models (LLMs) can tackle complex tasks through sustained environmental interaction. LLM-based agents are therefore increasingly used to tackle complex long-horizon...
arXiv:2608. 08255v1 Announce Type: new Abstract: Agentic reinforcement learning (RL) often suffers from delayed and sparse rewards in real-world environments.
Long-horizon large language model (LLM) agents are typically optimized with sparse terminal outcomes, making fine-grained credit assignment across multi-step interactions difficult. Existing approache...
arXiv:2608. 11967v1 Announce Type: cross Abstract: Large language model agents increasingly rely on long-horizon reasoning to solve complex tasks involving planning, tool use, and memory.
Tree-Aware Posterior Distillation for Long-Horizon Agentic Reinforcement Learning proposes TreeOPSD, a critic‑free framework that treats each interaction turn as a tree node, branches alternative continuations, and recursively backs up terminal rewards to generate implicit turn‑level credit. The method maps this credit to a bounded teacher distribution based on the likelihood ratio to a frozen behavior policy, and minimizes the divergence between teacher and student to recover policy‑gradient directions while limiting updates across mini‑batches. Empirical results show TreeOPSD enhances agentic performance on long‑horizon benchmarks, notably improving Qwen3‑30B‑A3B‑Thinking‑2507’s accuracy by 4.97 points over GRPO in the Deep Research setting.
arXiv:2607. 27973v1 Announce Type: new Abstract: Recently, Reinforcement Learning (RL) has emerged as a crucial paradigm for the post-training of Large Language Model (LLM) agents.