AI Briefing#reinforcement-learning
The latest AI and developer news about #reinforcement-learning, with the original source and a short summary.
The latest AI and developer news about #reinforcement-learning, with the original source and a short summary.
InternLM has released Intern-S2-Preview, a 35B scientific multimodal model.
Nvidia is co-developing reinforcement learning AI with UK startup Ineffable Intelligence.
A 6-DoF Gymnasium simulation environment for training rocket guidance, navigation, and control (GNC) algorithms has been released.
inclusionAI released Ring-2.6-1T, a 1-trillion-parameter reasoning model.
A paper on an online adaptation harness for self-improving agents has been released.
NVIDIA and Ineffable Intelligence are jointly designing reinforcement learning infrastructure.
FST improves both sample efficiency and the forgetting problem together in LLM continual learning.
Fine-tuning Qwen3.5-4B with RL for RLM achieved performance on par with Claude Sonnet 4.6.
The author compares SFT, RL, and OPD from a distributional perspective and emphasizes the importance of policy-based sampling.
Google researchers proposed SkillOS, which learns skill curation through experience-based RL.
Anthropic CEO Dario Amodei discusses the evolution of scaling laws and the compute hypothesis.
Google DeepMind is set to test AI models in EVE Online.
Ramp made Fast Ask faster and more accurate through Prime-RL post-training.
Frontier lab internal QC has become the new standard for RL data.
Fixed vLLM V1's logprob, cache, and update discrepancies to align with the V0 trajectory.
The LoRR 2026 competition, held in conjunction with AAMAS 2026, addresses large-scale multi-robot coordination challenges.
Hugging Face has released an interactive guide comparing the performance and scalability of various RL frameworks.
Applying Speculative Decoding to NeMo-RL boosted RL rollouts by 1.8x.
Microsoft has unveiled World-R1's 3D-consistent video generation results.
AlphaGo developer David Silver points out the limitations of LLMs and presents reinforcement-learning-based 'superlearners' as the next generation of AI.
DeepMind alum David Silver's Ineffable Intelligence has raised a record-breaking $1.1 billion seed investment.
Post-training made models more decisive, but it did not improve accuracy.
This introduces the causes of LLMs speaking with confident tones even when hallucinating, and MIT's research findings to address this.
A study that quantifies Over-Editing (excessive rewriting) in coding models and reduces it via RL.