Summary
This paper addresses the challenge of adapting reasoning models to new tasks with limited output-level supervision, where current methods like RLVR struggle with low initial success probabilities. It introduces a new loss family, $J_Q$, based on the Tsallis $q$-logarithm, which interpolates between exploitation-focused RLVR and density-estimation over latent trajectories. This approach aims to improve the training stability and effectiveness of reasoning models, particularly when starting from a low success rate.
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[Paper] Ruka-v2: Tendon Driven Open-Source Dexterous Hand with Wrist and Abduction for Robot Learning
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