The Role-Context-Task-Constraint (RCTC) model is the most effective framework for structured prompting, eliminating ambiguity by assigning a professional persona, providing context, specifying an impe…
Researchers propose Moir, a method that estimates the preservation covariance directly from a language model's own decoding distribution to prevent capability erosion during knowledge editing. In test…
In a head-to-head test of eight GSM8K math word problems, gpt-oss-120b and Kimi K3 tied on seven tasks, both returning the correct numeric answer every time. The only separation came on the eighth tas…
Researchers introduced PPO-HSC (Proximal Policy Optimization with High-order Sampling Coverage), an exploratory reinforcement learning framework that addresses mode collapse in Large Language Model fi…
Researchers propose PPL-Factory, a task-aware and budget-aware data selection framework for large language model fine-tuning that uses perplexity-based scores to select informative training samples. E…
A new paper from researchers systematically evaluating widely used AI safety datasets AdvBench and HarmBench finds that these benchmarks contain contrived data points with excessive triggering cues an…
Researchers from University of California, Santa Barbara, Carnegie Mellon University, LMSYS Org, and University of Wisconsin–Madison introduced the Length Value Model (LenVM), a token-level framework …
A developer known as flirp spent 21 GPU-hours training Qwen2.5-1.5B-Instruct to reason in latent space without decoding tokens, but the model's hidden-state reasoning collapsed into content-free attra…
A new paper on arXiv (2607.11937v1) introduces viable path entropy (VPE), a finite-budget measure of verified continuation capacity for intelligent systems under repeated reflection. In language-model…
A new study from arXiv reveals that post-training quantization can silently alter how large language models reason even when task accuracy is preserved. Analyzing 30,000 chain-of-thought outputs from …
Researchers introduce KV-PRM, a process reward model that reduces scoring cost from O(L²) to O(L) by reading the KV cache from LLM generation instead of re-encoding trajectory text, achieving up to 5,…
Researchers discovered that reasoning models like o1 and R1 often overthink, computing answers at around 30% of their chain-of-thought but continuing for the remaining 70%. The termination decision is…
TinyToT, a lightweight inference server compatible with Ollama, achieves 97% accuracy on a 35-question benchmark spanning graduate-level science, medicine, law, finance, and software engineering witho…
Researchers from Meta AI benchmarked the energy costs of inference for LLaMA models on NVIDIA V100 and A100 GPUs across up to 32 GPUs, finding that inference energy consumption is significant and ofte…
Researchers introduced Self-Review Reinforcement Learning (SRRL), a training framework that embeds a self-review step into each RL episode to help large language models learn from sparse feedback. SRR…
NVIDIA open-sourced Nemotron-Labs-TwoTower, a diffusion language model that generates text 2.42x faster than its autoregressive counterpart without retraining original weights. The model achieves 98.7…
Researchers compared process and outcome reward structures for reinforcement learning with verifiable rewards (RLVR) in small language models, finding that process-only supervision achieved 63.73% acc…
Researchers propose Trajectory-Aware Commit Gating (TACG), a training-free decoder for diffusion language models that uses trajectory-aware signals to decide when to commit tokens, improving accuracy …
MarkTechPost published a tutorial on training Gemma-3 for structured mathematical reasoning using Tunix GRPO, LoRA adapters, and GSM8K rewards. The workflow includes environment setup, prompt formatti…
Researchers introduced LearnStop, a cost-aware early-exit method for reasoning language models that learns when to stop computation based on online features like answer confidence and entropy. Across …