Self-Policy Distillation extracts a capability subspace from model gradients on correctness tokens, projects KV activations into it for self-generation, and fine-tunes LLMs to achieve up to 13-16% gains over baselines without external signals.
Star: Self-taught reasoner bootstrapping reasoning with reasoning
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GeoX is a self-play RL framework in which a single multimodal policy proposes and solves spatial problems as executable programs over image primitives, using verifiable rewards to improve base VLMs by up to 5.5 points without large curated data.
VPG-EA applies variational posterior guidance and efficiency-aware distillation to compress LLM reasoning chains while preserving performance.
citing papers explorer
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Self-Policy Distillation via Capability-Selective Subspace Projection
Self-Policy Distillation extracts a capability subspace from model gradients on correctness tokens, projects KV activations into it for self-generation, and fine-tunes LLMs to achieve up to 13-16% gains over baselines without external signals.
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GeoX: Mastering Geospatial Reasoning Through Self-Play and Verifiable Rewards
GeoX is a self-play RL framework in which a single multimodal policy proposes and solves spatial problems as executable programs over image primitives, using verifiable rewards to improve base VLMs by up to 5.5 points without large curated data.
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Efficient LLM Reasoning via Variational Posterior Guidance with Efficiency Awareness
VPG-EA applies variational posterior guidance and efficiency-aware distillation to compress LLM reasoning chains while preserving performance.