FLEET reduces redundant sampling in language models through entropy-aware trajectory memory
FLEET addresses a fundamental inefficiency in LLM sampling: temperature-based generation produces redundant outputs as sample counts grow, wasting compute on semantic duplicates. The method introduces memory into the generation loop by tracking high-entropy token trajectories and using them to reweight logits per token, steering the model away from previously explored regions. This shifts sampling from memoryless aggregation toward informed exploration, directly improving sample efficiency and accuracy. The work matters because it challenges the assumption that scaling samples linearly improves results, suggesting instead that trajectory-aware generation could reduce inference cost while maintaining or improving quality across diverse benchmarks.
Modelwire context
ExplainerFLEET's core contribution isn't just better sampling, but the recognition that standard temperature-based generation treats each sample independently, creating semantic redundancy that scales with batch size. The method reintroduces state into what has been treated as a stateless process.
This connects directly to the memory-aware reasoning pattern emerging across recent work. Like FRESH (the failure-aware retrieval framework from last week) which preserves causal context around agent mistakes rather than treating them as isolated events, FLEET preserves trajectory context during generation rather than aggregating samples blindly. Similarly, the THESEUS framework for KGQA decouples reasoning paths from outcomes, allowing step-by-step navigation without task-specific tuning. FLEET applies that same principle to token-level generation: the path through logit space matters as much as the final answer. All three papers reject the assumption that you can treat inference as a sequence of independent decisions.
If FLEET's efficiency gains hold on long-form generation benchmarks (code, math, summarization) where redundancy compounds, that validates the core claim. If the method degrades on diversity-critical tasks (creative writing, open-ended QA), that signals the approach trades exploration for efficiency in ways that matter downstream.
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