Presents the first fully open pipeline for clinical LLMs by unifying eight public QA datasets with three clinician-vetted synthetic extensions and applying it to five base models to achieve benchmark gains while maintaining auditability.
Qwen3 technical report
5 Pith papers cite this work. Polarity classification is still indexing.
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BARISTA introduces a densely annotated egocentric coffee-preparation video dataset and multi-task benchmark that reveals performance variation across models on compositional visual tasks.
EvoEnv lets a single policy synthesize, validate, and use Python environments with durable solve-verify asymmetry to improve reasoning performance on Qwen3-4B-Thinking from 72.4 to 74.8 while fixed-data baselines decline.
MindLoom synthesizes frontier-level reasoning data by decomposing solutions into thought mode chains, training a retrieval model for mode selection, composing new problems with distribution-aligned sampling, and applying rollout-based difficulty labeling for fine-tuning.
Prune-OPD detects prefix drift via top-k overlap and dynamically prunes unreliable teacher rewards in OPD, cutting training time 37.6-68% on AMC/AIME/HMMT while preserving performance.
citing papers explorer
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Fully Open Meditron: An Auditable Pipeline for Clinical LLMs
Presents the first fully open pipeline for clinical LLMs by unifying eight public QA datasets with three clinician-vetted synthetic extensions and applying it to five base models to achieve benchmark gains while maintaining auditability.
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BARISTA: A Multi-Task Egocentric Benchmark for Compositional Visual Understanding
BARISTA introduces a densely annotated egocentric coffee-preparation video dataset and multi-task benchmark that reveals performance variation across models on compositional visual tasks.
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Learning to Build the Environment: Self-Evolving Reasoning RL via Verifiable Environment Synthesis
EvoEnv lets a single policy synthesize, validate, and use Python environments with durable solve-verify asymmetry to improve reasoning performance on Qwen3-4B-Thinking from 72.4 to 74.8 while fixed-data baselines decline.
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MindLoom: Composing Thought Modes for Frontier-Level Reasoning Data Synthesis
MindLoom synthesizes frontier-level reasoning data by decomposing solutions into thought mode chains, training a retrieval model for mode selection, composing new problems with distribution-aligned sampling, and applying rollout-based difficulty labeling for fine-tuning.
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Prune-OPD: Efficient and Reliable On-Policy Distillation for Long-Horizon Reasoning
Prune-OPD detects prefix drift via top-k overlap and dynamically prunes unreliable teacher rewards in OPD, cutting training time 37.6-68% on AMC/AIME/HMMT while preserving performance.