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This paper introduces TKFQA, a counterfactual benchmark of 10,130 QA pairs over tables, texts, and knowledge graphs for evaluating LLM factuality consistency and order-robust reasoning, and proposes ORLF, a training framework that improves reasoning-chain accuracy and reduces input-order sensitivity.
iFAN is a training framework that improves mask transformers for segmentation by aligning query ranking with mask quality and distilling intermediate predictions to the final layer, yielding consistent gains across benchmarks.
MiniWorld is a reproducible framework for training video world models from scratch using a block-causal Video Diffusion Transformer with Flow Matching, enabling efficient streaming generation and trainable in days on a single 8-GPU server.
Molt is a PyTorch-native training framework for agentic reinforcement learning designed to be compact and clean for easy modification, while achieving performance comparable to Megatron-based stacks.
Introduces a framework for training a harness to improve task LLM capabilities in a model-agnostic and task-environment-agnostic way, with results on Terminal Bench and SWE-Bench.
PRIME-RL is a framework for large-scale asynchronous reinforcement learning, designed to be hackable and scale to 1000+ GPUs with support for various models and environments.
Introduces Pyligent, a training framework that uses task validators to label failures and teaches LLMs to backtrack during reasoning, improving solve rates on hidden graphs, Sudoku, and Blocksworld.
DeepSeek open-sourced DeepSpec, a full-stack codebase for training and evaluating draft models for speculative decoding, enabling 60-85% faster generation. It includes data preparation, training, and evaluation scripts with support for multiple draft model algorithms (DSpark, DFlash, Eagle3).
This paper introduces RPCL, a training-only framework for robust pair confidence learning in multimodal emotion-cause pair extraction, which improves discriminative separation of gold pairs from hard negatives and achieves significant gains in Pair F1 and AUPRC on three datasets.
A call for open training frameworks in AI research, introducing FeynRL, a modular and explicit framework for RL post-training of LLMs, VLMs, and agents, designed to make training processes visible and modifiable.
This paper introduces RePro, a framework that trains LLM agents to self-generate progress signals through a forward-then-reflect rollout paradigm, achieving up to 12% absolute success rate gains on WebShop, ALFWorld, and Sokoban benchmarks.
Orchard is an open-source framework for scalable agentic modeling that enables training diverse autonomous agents, achieving state-of-the-art results on coding, GUI navigation, and personal assistance tasks.
EasyVideoR1 is an efficient reinforcement learning framework for training large vision-language models on video understanding tasks, featuring offline preprocessing with tensor caching for 1.47x throughput improvement, a task-aware reward system covering 11 problem types, and evaluation across 22 video benchmarks. It also supports joint image-video training and a mixed offline-online data training paradigm.
Agent Lightning introduces a flexible reinforcement learning framework for training large language models in AI agents, achieving decoupling between agent execution and training to handle complex interactions.
OpenAI releases RL-Teacher, an open-source tool for training AI systems through human feedback instead of hand-crafted reward functions, with applications to safe AI development and complex reinforcement learning problems.