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DHRCL proposes a reinforcement learning framework with dense hierarchical rewards and curriculum learning for training code LLMs, using syntax validation, execution success, unit-test pass rate, and AST structural similarity in a three-stage curriculum.
StickyMoE proposes a differentiable routing consistency loss that encourages adjacent tokens to activate the same experts in MoE models, reducing expert-swapping overhead and cache misses during inference on edge devices by up to 3.92× while improving perplexity.
The paper introduces AUF (Accept-Until-Fail), a simple modification to the cross-entropy loss for masked block drafters in speculative decoding that restricts supervision to the prefix up to the first predicted failure, improving average emitted length across benchmarks without changing inference.
Introduces StoMPP, a layerwise progressive freezing training scaffold for binary neural networks that gradually binarizes layers from input to output, achieving significant accuracy improvements over vanilla STE without relying on straight-through estimator, and shows consistent gains across various architectures and tasks.
A 4 billion parameter open model from the Apodex family outperforms 30 billion parameter models on web research benchmarks, attributed to careful training data and self-verification techniques rather than raw scale, suggesting a more democratic trajectory for AI capability.
GDSD proposes a reinforcement learning method that directly distills denoisers from advantage-guided self-teachers for diffusion language models, avoiding biases from ELBO-based likelihood surrogates. It achieves up to +19.6% accuracy improvements on planning, math, and coding benchmarks over prior state-of-the-art methods.
Dynamic Fine-Tuning (DFT) is introduced as a method that reweights the SFT loss using the model's own token probability, creating a feedback loop, and adds forward KL to penalize tokens the base model finds likely but the policy has pushed toward zero probability. The tweet expresses skepticism about SFT papers in practice but praises the attempt.
This tweet announces Fast-Slow Training (FST), a new continual learning method that treats model parameters as slow weights and optimized context as fast weights, reportedly outperforming weights-only training on math, code, and general reasoning benchmarks.