Training open-weight LLMs on conversational serializations of authentic student programming submissions produces artificial learners that better replicate real debugging behavior than code-only baselines or prompted large models.
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Diffusion models improve generation quality via inference-time search over noise candidates guided by verifiers and algorithms, yielding gains beyond denoising step scaling on class- and text-conditioned benchmarks.
LambdaPO introduces pairwise preference-based advantage estimation and a semantic density reward to extract more optimization signal from trajectory groups than GRPO's monolithic baseline.
Diffusion-APO synchronizes training noise with inference trajectories in video diffusion models to improve preference alignment and visual quality.
citing papers explorer
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Teaching Language Models How to Code Like Learners: Conversational Serialization for Student Simulation
Training open-weight LLMs on conversational serializations of authentic student programming submissions produces artificial learners that better replicate real debugging behavior than code-only baselines or prompted large models.
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Inference-Time Scaling for Diffusion Models beyond Scaling Denoising Steps
Diffusion models improve generation quality via inference-time search over noise candidates guided by verifiers and algorithms, yielding gains beyond denoising step scaling on class- and text-conditioned benchmarks.
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LambdaPO: A Lambda Style Policy Optimization for Reasoning Language Models
LambdaPO introduces pairwise preference-based advantage estimation and a semantic density reward to extract more optimization signal from trajectory groups than GRPO's monolithic baseline.
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Diffusion-APO: Trajectory-Aware Direct Preference Alignment for Video Diffusion Transformers
Diffusion-APO synchronizes training noise with inference trajectories in video diffusion models to improve preference alignment and visual quality.