New arXiv work introduces Atomic-PPO, a deep reinforcement learning method that scales ride matching, repositioning, and charger allocation for electric robo-taxi fleets under stochastic demand. It…

#RoboTaxis #ReinforcementLearning #ElectricFleets #AIResearch
https://arxiv.org/abs/2502.13392

After receiving early access to NVIDIA's #Nemotron 3.5 Super 120B VL, we extended the model with #agentic web development capabilities via #reinforcementlearning. Our fine-tune delivers better results at lower token costs. You can find out more about our experiment: huggingface.co/blog/tngtech...

A new open platform called MAI-Gym unifies GUI agent evaluation and online RL training across mobile, computer, and web environments, supporting ten benchmarks and scaling to 20,000 parallel…

#AIInfrastructure #MachineLearning #OpenSource #ReinforcementLearning
https://arxiv.org/abs/2610.20939

arXiv paper CoCA reframes rubric-based RL for LLMs as criterion-aware credit assignment, using rubric info as a relevance cue rather than self-distillation targets to avoid reward degradation. A useful step…

#OpenSourceAI #ReinforcementLearning #LLMs #AIResearch
https://arxiv.org/abs/2610.17733

A new framework called MCPDisCo uses ecosystem-scale reinforcement learning to train MCP-native agents across 70 servers and 527 tools, while addressing a "reasoning-tool seesaw" failure mode where gains in one…

#MCP #AIAgents #ReinforcementLearning #DevTools
https://arxiv.org/abs/2610.14956

A new arXiv paper proposes REGEN, a method for distilling knowledge from expert models into generalist ones using offline reinforcement learning, aiming to cut the infrastructure and cost demands of large-scale online…

#AI #LLMs #ReinforcementLearning #Distillation
https://arxiv.org/abs/2607.19450

Labelbox raises more than $100 million at $1.25 billion pre-money valuation Labelbox announced more than $100 million in Series E financing,...

https://www.narrowit.com/news/labelbox-series-e-financing-valuation-2026-10-14
#Labelbox #SeriesE #ArtificialIntelligence #ReinforcementLearning

Why is synthetic data "a big mistake?"

Watch ACM Turing Laureate Richard Sutton share his take.

Source: Sequoia Capital YouTube Channel "Rich Sutton and Khurram Javed: Why AI Models Stop Learning, and How to Start It Again"

#syntheticdata #reinforcementlearning #generativeai

Alibaba Qwen Introduces QwenGyre: Elastic Reinforcement Learning RL for XLong-Horizon Agents #Technology #AI #GenerativeAI #alibaba #qwengyre #reinforcementlearning #codingagents #aitechnology

https://puretech.news/read?id=235535101771907072

The EliteTable algorithm, using LLMs for executable control policies, hit the search-score ceiling in CartPole-v1 in the first generation of all ten independent searches. Improvements were observed in five of six tasks, while six out of ten Acrobot-v1 sea…

#LLM #ReinforcementLearning #ProgramSearch

The new SMITH methodology unifies tool generation and invocation training within a single policy, employing three independent reward axes to correct errors in schema, code, and outcome. Testing was conducted on a 4 billion parameter Qwen3 model.

#LLM #ToolUSE #ReinforcementLearning

Harvard University (@harvard.edu) is hiring:
Postdoctoral Fellow in Reinforcement Learning with Applications in Digital Interventions

#postdoc #statistics #reinforcementlearning

A new paper introduces Proof-of-Use, a reinforcement learning framework that tackles tool-call hacking in deep research agents by enforcing causal dependency between retrieved evidence and reasoning.

#AIResearch #MachineLearning #DeepResearch #ReinforcementLearning
https://arxiv.org/abs/2510.10931

New arXiv paper proposes Q-Shaped Options for Hierarchical Reinforcement Learning, addressing how agents learn temporal and state abstractions for long-horizon, goal-conditioned tasks. It argues current HRL…

#AI #ReinforcementLearning #MachineLearning #Research
https://arxiv.org/abs/2610.12135

New arXiv paper proposes a 3D framework characterizing AI puzzle-solving methods via MDP formalism, autonomy, and computational cost, comparing Neurosolver, FBRL, and an LLM-based approach. Useful lens for evaluating…

#AI #ReinforcementLearning #LLMs #Arxiv
https://arxiv.org/abs/2610.11696

New work proposes constrained command-conditioned PPO for MicroRTS, letting RL strategies adapt to varied opponents via discrete high-level commands and a shared executor. Worth a look for those training brittle RTS agents.

#reinforcementlearning #rts #microRTS
https://arxiv.org/abs/2610.11663

The SELF framework from arXiv 2610.11384 proposes jointly training environmental feedback modeling with hindsight self-distillation for language agents, improving learning from environments that lack explicit rewards.

#Robotics #AI #ReinforcementLearning #LLMAgents
https://arxiv.org/abs/2610.11384

A new arXiv paper formalizes Adversarial Heuristic Learning, where AI agents refine game policies without updating model weights. The benchmark AAArena includes 12 games and 1,920 archived human programs for…

#AIagents #GameAI #ReinforcementLearning #Benchmark
https://arxiv.org/abs/2610.12341

SynCo is a multi-agent reinforcement learning framework that co-trains a data Synthesizer and a learner LLM so training tasks stay matched to the model's evolving capabilities, rather than relying on static…

#LLMs #ReinforcementLearning #AIAgents #DataSynthesis
https://arxiv.org/abs/2610.11345

Prof. Zhang's team proposes a two-stage safety framework for offline RL-based #sepsistreatment employing Constraint-Penalized Q-learning combined with Implicit Q-Learning (CPQ-IQL) and a runtime safety filter. Please access the full text at

www.sciltp.com/journals/tai...

#reinforcementlearning