Evidence that RL teaches genuinely new abilities: compositional skills emerge and transfer across tasks Does RL merely reweight what an LLM already knows—or can it teach genuinely new skills? This paper offers concrete evidence for the latter. Using a controlled, synthetic framework, the authors define “skills” as string transformation
A structured 80k instruction–image corpus spanning 11 domains and 51 subtasks to train unified visual editors Unified models for image generation and editing hit a data ceiling: existing corpora emphasize basic manipulations but miss real‑world complexity. OpenGPT‑4o‑Image tackles this with a hierarchical task taxonomy and automated
ROVER replaces PPO loops with uniform‑policy Q‑values—boosting quality and diversity in math reasoning Popular RLVR methods for LLM reasoning lean on generalized policy iteration (e.g., PPO/GRPO), but suffer instability and diversity collapse. This paper reframes math RLVR as a specialized finite‑horizon MDP with deterministic
A dynamic, self‑paced curriculum that restructures problems to match model ability in RLVR Online RL with Verifiable Rewards (RLVR) has boosted LLM reasoning—but most methods treat all problems equally, wasting effort on solved items and flailing on those beyond current capability. CLPO fixes that with a dynamic pedagogy:
AutoPlay explores environments to synthesize diverse, verifiable tasks—fueling stronger UI agents Agent post‑training hinges on rich, grounded tasks—but human‑curated datasets are costly and often shallow. AutoPlay introduces a scalable alternative: let an MLLM‑based explorer systematically probe interactive environments, discover states and affordances, and then synthesize
A dual‑track “brain‑mouth” design for unified multimodal understanding and expressive, streaming voice MGM‑Omni unifies multimodal reasoning with long‑horizon, controllable speech generation in a single Omni LLM. Rather than bolt speech onto a text reasoning core, it uses a dual‑track, token‑based “brain‑mouth” architecture that
From tabula rasa to transparent mastery: an LLM agent that induces rules and writes its own playbook Most deep RL agents master games by amassing opaque experience. CEL—Cogito, ergo ludo—takes a different path: it learns to play by reasoning and planning. Powered by a Large Language Model, CEL
DataMind: a scalable data synthesis and agent training recipe powering open generalist analytics agents Generalist data‑analytic agents promise automated, end‑to‑end insights across messy, large, and diverse files. But open models often falter on long‑horizon, code‑based reasoning and multi‑turn tool use. DataMind introduces a full
RL from Human Interaction links long‑term personas to turn‑level preferences for scalable, personalized alignment Most alignment today leans on expert‑crafted datasets and static preferences. This paper proposes Reinforcement Learning from Human Interaction (RLHI): learning directly from in‑the‑wild user conversations. Instead of treating chats as fixed
High-quality data over sheer quantity: sub‑billion models that reason competitively with only ~2T core tokens Do we really need tens of trillions of tokens to unlock reasoning in small models? MobileLLM‑R1 argues no. By carefully selecting and resampling about ~2T tokens of high-quality open data, then pretraining to
Evidence that RL teaches genuinely new, compositional skills—beyond mere reweighting. Does RL truly endow LLMs with new capabilities—or just reweight what’s already there? This study provides concrete evidence for genuine skill acquisition via composition. Using a controlled synthetic setup, a “skill” is defined as computing a string
80k instruction–image pairs across 11 domains and 51 subtasks, built with systematic taxonomy. Unified multimodal models stumble when their training data underrepresents real-world complexity. OpenGPT-4o-Image answers with a large-scale dataset constructed via a hierarchical task taxonomy and automated generation pipeline. Beyond basics like text rendering and style transfer, it