TRN-R1-Zero is an RL-only post-training method that lets LLMs perform zero-shot node, edge, and graph reasoning on text-rich networks without supervised data or larger-model distillation.
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Active information seeking via search tools, when combined with multi-candidate context pruning during training, produces consistent gains on translation, health, and reasoning tasks over naive tool addition or no-tool baselines.
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TRN-R1-Zero: Text-rich Network Reasoning via LLMs with Reinforcement Learning Only
TRN-R1-Zero is an RL-only post-training method that lets LLMs perform zero-shot node, edge, and graph reasoning on text-rich networks without supervised data or larger-model distillation.
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Context Training with Active Information Seeking
Active information seeking via search tools, when combined with multi-candidate context pruning during training, produces consistent gains on translation, health, and reasoning tasks over naive tool addition or no-tool baselines.