Reinforcement learning lacks foundation models despite synthetic MDPs being feasible. A proof-of-concept shows a single model trained on synthetic MDPs solves tabular benchmarks without tuning, outperforming existing methods in online settings and matching them offline.
Reinforcement Learning Foundation Models Should Already Be A Thing
RACL: Reasoning-Agent Control Layer for Metaheuristic Learning
RACL introduces a reasoning agent that controls metaheuristic search behavior without replacing optimizers or altering constraints. It improves or ties key policies in vehicle routing experiments, reducing average cost by 8.337% versus Fixed and 1.605% versus Stagnation-Triggered policies, with no significant computational overhead.
Training LLMs for Long-Lifecycle Agents via Cross-Domain Generalization
A new framework enables large language models to develop 'Connect the Dots' capability, allowing long-lifecycle agents to learn from experiences and iteratively update their environment context. The framework uses reinforcement learning with long rollout sequences and custom tasks to promote cross-domain generalization, showing effective out-of-distribution performance in both domains and transition settings.
Training LLMs for Long-Lifecycle Agents via Cross-Domain Generalization
A new framework enables large language models to learn 'Connect the Dots' by using reinforcement learning with long rollout sequences. The method includes tailored tasks and environments to foster meta-capability development, showing strong cross-domain generalization and performance in out-of-distribution settings. Implementations are available at https://github.com/agentscope-ai/Trinity-RFT/tree/research/cod/examples/research_cod.
Frontier Post-Training Recipe Review with Finbarr Timbers
The podcast reviews the evolution of post-training recipes in large language models, from InstructGPT to 2026 frontier models. It highlights Multi-Teacher On-Policy Distillation (MOPD) as the dominant pattern, where domain-specialist models are trained and then distilled into a general student model via on-policy distillation, scaling to over 10 teachers in models like DeepSeek V4 and Nemotron 3 Ultra.
ARCO: Adaptive Rubric with Co-Evolution for Multi-Step LLM Agents
ARCO introduces a rubric framework that enables step-level credit assignment for multi-step LLM agents. It jointly updates a shared model with generation and scoring heads, allowing the rubric content and scoring function to co-evolve via on-policy data, improving performance and interpretability across benchmarks.