reinforcement-learning

New research from Tencent introduces Recursive Synthetic Terminal Tasks (RST), a method that progressively generates harder, verifiable training tasks for AI agents. Starting from 639 tasks, it produced 37,484 verified tasks across 15 rounds, and reinforcement learning with these tasks improved Qwen3.5-27B from 22.7% to 32.0% on Terminal-Bench Hard.

Prime Intellect releases prime-rl 0.8.0, adding multi-agent training, Nixl and model express weight broadcast, extended multimodal support, and various performance improvements.

Prime RL now supports expressing and training multi-agent systems, enabling use cases like agentic judge, self-play, user simulation, and complex agent collaboration.

This paper proposes OG-SPR, a model-free visual RL algorithm that combines latent self-prediction with observation prediction to learn dynamics-aware representations, achieving improved sample efficiency on DeepMind Control Suite tasks.

The paper introduces TSR, a framework that decomposes social dialogue into strategic planning and linguistic execution, and LHRL-VGR, a reinforcement learning algorithm with variance-gated rewards. Fine-tuning a Qwen2.5-7B agent with this approach surpasses the GPT-4o baseline by 7.32% in goal completion on the SOTOPIA benchmark.

This paper introduces M3R-Bench, a unified evidence-grounded benchmark for multimodal metaphor understanding with 1,000 image-text instances, and proposes M3R-Reasoner, an 8B-parameter model combining curriculum-based reasoning supervision and reinforcement learning that outperforms larger proprietary models.

This paper introduces LC-GRPO, a flow-based GRPO framework with Langevin correction that bridges the train-inference gap by aligning stochastic training rollouts with deterministic ODE sampling, improving reward optimization on models like SD3.5, FLUX.1-Dev, and HunyuanVideo.

Introduces EvoHarness-RL, a framework that learns runtime harness policies for long-horizon LLM agents, enabling them to construct and update external state (belief, progress, experience) during task execution. Using Qwen3-8B on ALFWorld, it achieves 96.9% success and reveals harness annealing and evolution dynamics.

This paper introduces IFlowNets, extending Adversarial Flow Networks to incomplete information games, proving prior constraints invalid and showing comparable or better performance than existing methods in preliminary experiments.

Presents an emerging retail portfolio management application that uses personalized, tax-aware reinforcement learning with natural language goal input, featuring a three-phase pipeline and integration with live brokerage APIs.

RA-CAD presents a state-aware agent for text-to-CAD generation that uses a Generate–Execute–Critique–Rewrite loop, with feedback-driven agent optimization via Group Relative Policy Optimization. It achieves state-of-the-art execution validity and geometric quality on CADFusion and Text2CAD benchmarks.

SkillHEX proposes a closed-loop framework for autonomous skill evolution in LLM agents, using hypothesis-driven self-verification and evidence-guided tree search to overcome sparse reward challenges. It outperforms existing self-evolving methods on SkillsBench with limited interaction budgets.

Search2Skill is a framework that trains LLM agents to identify capability gaps, search external sources, and distill retrieved knowledge into reusable skills using rubric-based reinforcement learning, beating baselines on expert-domain benchmarks.

Factorio Learning Environment v0.3.0 is an open-source platform for evaluating AI agents in Factorio, adding headless scaling, OpenAI Gym compatibility, and Claude Code integration for live demonstrations.

A researcher highlights that meta-RL is a promising direction for training LLM agents, reframing agent training as a cross-episode meta-RL problem to enable active exploration and trial-and-error adaptation.

This paper proposes GOAL, a constraint-aware generative framework for incentivized advertising that formulates incentive allocation as conditional sequence generation, and introduces SCPO to learn a single generative policy that generalizes across ROI constraints. Experiments show improved long-term revenue and user retention with reduced ROI violations.

Introduces SPOT, a method for on-policy distillation that uses sparse probing and outcome calibration to improve reasoning performance in smaller student models while balancing solution quality and coverage.

The paper proposes EASy, a trainable agentic framework that uses reinforcement learning to jointly optimize task performance and computational efficiency, introducing a milestone-plan-act workflow, dependency-aware execution graphs, and tree-structured rollout for training.

This paper introduces ATLAS, a model-based continual reinforcement learning algorithm that combines Grow When Required networks with Successor Features to achieve high sample efficiency and robust adaptation to non-stationary environments, demonstrating positive backward transfer in spatial navigation tasks.

This paper proposes an adaptive training controller for CVaR risk-aware Q-learning, improving finite-budget behavior, reducing Bellman residuals by ~85%, and yielding better risk-adjusted performance in daily Bitcoin trading.