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Life sciences · Preprint
arXiv · August 12, 2026
Posted before peer review. The findings may change or fail to hold.
LoongReflect is a proposed training framework for improving long-horizon reasoning in language model agents through a dual-channel learning mechanism combining global distillation with outcome-based reinforcement learning. The authors report consistent improvements over baselines on multi-hop retrieval and mathematical reasoning tasks, but the work is unrefereed and lacks peer-reviewed validation.
Preprint. Intervention: LoongReflect training framework combining global perspective distillation and outcome-based GRPO optimization. Compared with: Outcome-only reinforcement learning and self-distillation baselines.
LoongReflect demonstrates consistent improvements over outcome-only reinforcement learning and self-distillation baselines on multi-hop retrieval-augmented generation and mathematical reasoning benchmarks The framework formulates reflection as a memory-control policy operating over a reversible trajectory tree with explicit reflect and backtrack actions A fast channel distills globally informed reflective behavior from a privileged teacher; a slow channel optimizes complete trajectories using outcome-based GRPO
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This is an unrefereed preprint describing a novel machine learning framework for training language model agents; it reports experimental results but lacks peer review and clinical/medical application.
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Large language model agents increasingly rely on long-horizon reasoning to solve complex tasks involving planning, tool use, and memory. A critical capability in such settings is reflection: assessing trajectory progress, identifying missing evidence and unreliable intermediate states, and deciding whether to continue, revise, or abandon the current branch. Learning effective reflection, however, is challenging because reflection is performed locally within the current branch, whereas its utility can only be determined by its contribution to the final trajectory outcome. This local-global mismatch makes outcome-based reinforcement learning provide only local, sparse and delayed supervision for reflective decisions. To solve these, we propose LoongReflect, a training framework that formulates reflection as a memory-control policy. The agent operates over a reversible trajectory tree using explicit reflect and backtrack actions. Reflection consolidates verified facts, missing evidence, and branch-specific risks into working memory, while backtracking removes an unreliable branch from the active context and preserves a concise corrective lesson. To learn this policy, LoongReflect combines two complementary signals through a look-ahead, extragradient-style coordination mechanism. A fast channel distills globally informed reflective behavior from a privileged teacher, with supervision restricted to reflection and backtracking tokens. A slow channel optimizes complete trajectories using outcome-based GRPO, aligning local control decisions with final task success. Experiments on multi-hop retrieval-augmented generation and mathematical reasoning benchmarks demonstrate consistent improvements over outcome-only reinforcement learning and self-distillation baselines.
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