# EVAThink

EVAThink turns whether a sampled set of frames is enough to answer directly into a training signal: for the same question and answer it builds evidence-dense and evidence-sparse views of a video and keeps only pairs where direct answering degrades on the sparse view but explicit reasoning recovers accuracy, then uses two-stage reinforcement learning to teach the model to switch between direct answering and reasoning. Trained on only 6.7K samples, it reaches 63.5% and 65.6% average across six video understanding benchmarks under fixed 32- and 64-frame budgets, both above the Qwen3-VL-8B-Instruct base; at 64 frames the adaptive mode beats direct-only and think-only by 2.3 and 3.6 percentage points and cuts output length by about 62% relative to think-only.

EVAThink turns whether a sampled set of frames is enough to answer directly into a training signal: for the same question and answer it builds evidence-dense and evidence-sparse views of a video and keeps only pairs where direct answering degrades on the sparse view but explicit reasoning recovers accuracy, then uses two-stage reinforcement learning to teach the model to switch between direct answering and reasoning. Trained on only 6.7K samples, it reaches 63.5% and 65.6% average across six video understanding benchmarks under fixed 32- and 64-frame budgets, both above the Qwen3-VL-8B-Instruct base; at 64 frames the adaptive mode beats direct-only and think-only by 2.3 and 3.6 percentage points and cuts output length by about 62% relative to think-only.