GEAR reshapes GRPO trajectory advantages using divergence signals from a ground-truth-conditioned teacher to create adaptive token- and segment-level credit regions.
React: Synergizing reasoning and acting in language models
2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2verdicts
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AgenticAITA proposes a training-free multi-agent LLM framework for autonomous trading using a deliberative pipeline, Z-score triggers, and safety gates, shown to run correctly in a five-day live dry-run with 157 invocations.
citing papers explorer
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GEAR: Granularity-Adaptive Advantage Reweighting for LLM Agents via Self-Distillation
GEAR reshapes GRPO trajectory advantages using divergence signals from a ground-truth-conditioned teacher to create adaptive token- and segment-level credit regions.
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AgenticAITA: A Proof-Of-Concept About Deliberative Multi-Agent Reasoning for Autonomous Trading Systems
AgenticAITA proposes a training-free multi-agent LLM framework for autonomous trading using a deliberative pipeline, Z-score triggers, and safety gates, shown to run correctly in a five-day live dry-run with 157 invocations.