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ByteShape released ShapeLearn GGUFs for Qwen 3.8 27B, achieving high accuracy on benchmarks, and discussed the role of KLD in quantization, with a related paper accepted at EMNLP 2026.
This paper proposes a principled communication gating mechanism for multi-agent reinforcement learning using KL divergence between agents' belief distributions, showing performance improvements and interpretability on benchmarks like Predator-Prey and MPE.
This paper introduces Fisher8, an output-layer gradient correction that uses Fisher geometry instead of Euclidean geometry to stabilize neural heteroscedastic regression, improving uncertainty calibration and likelihood-error tradeoffs.
A detailed benchmark comparing 16 quantizations of Qwen3.6 27B across GGUF, NVFP4, AWQ, AutoRound, and FP8 formats, measuring KL divergence from the unquantized reference. Weight-only GGUF quants generally offer the best quality-size tradeoffs, while vLLM quants vary substantially.
KLQ is a training-free LLM quantization method that allocates bits per direction based on measured KL divergence, outperforming existing training-free rotation-based methods on W4A4KV4-bit settings for models like Llama 3.2 1B and Qwen 2.5.
atomic.chat released 14 quantized GGUF builds of DeepSeek V4 Flash 0731, from lossless BF16 to 1-bit. They recommend AD-IQ2_M for 128GB hardware, which matches the original's token choice 83.6% of the time.
A developer built a testing harness that measures KL divergence per weight group during quantization, leading to three custom quantized builds of Qwen3.6-27B (Bedrock, Tightrope, Gambit) with optimized compression. Tool calling is identified as the first capability to degrade under quantization.
This paper establishes a rigorous quantitative connection between neural network score function approximation and the resulting distribution approximation in score-based diffusion models, proving that accurate score approximation leads to close distribution approximation in KL divergence, with an explicit bound.
Discusses the trade-offs of using Kullback-Leibler divergence in quantitative analysis, framing it as a Hamlet-like dilemma for quants.
This tweet lists 7 fundamental math ideas used daily by ML engineers, with brief explanations emphasizing the underlying derivations, such as why gradient descent moves in the steepest direction and why softmax plus cross-entropy yields a clean gradient.
This article explains components of On Policy Distillation loss functions, including forward vs reverse KL divergence, supervision granularity, privilege types, and privilege advantage estimation. Additional resources from Thinking Machines, Hugging Face, and others are provided.
Identifies two coupled causes of posterior collapse in VAEs and introduces λ-VAE, a modification to the reparameterization step that equalizes variance across latent dimensions, reducing collapse and improving information capacity.
Proposes Asymmetric Mutual Variational Learning (AMVL) to resolve train-inference mismatch in multimodal continuous reasoning by using bidirectional calibration to prevent answer leakage and improve latent-space stability, achieving significant gains on the BLINK benchmark.
This paper shows that the gradient of the clipped surrogate in Proximal Policy Optimization (PPO) is exactly reproduced by a per-sample Kullback-Leibler penalty with a variable coefficient, revealing structural features of the clipped surrogate and suggesting new design directions.
This paper reveals that aggressive post-training quantization of reasoning models leads to increased overthinking errors, where models reach correct intermediate answers but fail to finalize them. A simple logit penalty on overthinking markers reduces chain-of-thought length by 12-23% while improving accuracy, especially for quantized models.
KL Zero is an interactive browser game where players draw a probability distribution to match a target KL divergence value, helping users intuitively understand the concept of KL divergence in machine learning.
Proposes EKSFT, a selective fine-tuning method for large language models that masks tokens with high entropy or high KL divergence from a reference model, preserving pre-trained distribution while injecting task knowledge. Experiments on mathematical reasoning benchmarks show it outperforms standard SFT and improves subsequent RL fine-tuning.
Trust Region Q-Adjoint Matching (TRQAM) addresses instability in off-policy reinforcement learning by adaptively controlling path-space KL divergence through projected dual descent, enabling stable fine-tuning of pretrained flow policies. The method consistently outperforms prior arts on 50 OGBench tasks, achieving a 68% success rate in offline RL compared to the strongest baseline's 46%.
This paper introduces on-policy distillation, which trains a student model on its own trajectories with teacher token-level KL supervision to fix train-inference mismatch, unifying forward-KL, reverse-KL, and JSD losses, with reverse-KL favored for smaller students.
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.