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RadixArk launches Miles v0.1, an open-source reinforcement learning framework for large language models and multimodal models, aimed at simplifying and scaling RL training.
The paper introduces ARC, a training recipe for fairer relative advantage comparison in open-ended real-world interaction by conditioning rollouts on strategy, and presents INTER3, a paradigm for responsive user-agent interaction that reduces latency.
Thinking Machines releases Inkling, an open-source multi-modal reasoning model with innovations in post-training RL, achieving stable scaling to 30M+ rollouts and controllable thinking effort. The model exhibits compressed reasoning and will be available soon for fine-tuning.
The author trained a Qwen3.6-35B-A3B model using reinforcement learning to then RL-train small task-specific Qwen models, and has released everything fully open source.
This article presents a recipe for low-precision (NVFP4) RL training that balances throughput and stability, addressing issues from forward and backward pass quantization errors.
Dockerless is a new method that enables reinforcement learning for coding agents without requiring environment setup by using an agentic verifier to explore the repository and score patches as rewards.
This paper systematically studies layer-wise contribution in RL post-training for LLMs, finding that training a single middle transformer layer can recover or even surpass full-parameter RL gains, with consistent patterns across models and tasks.
Discusses how sandbox startup latency and scaling in RL training infrastructure can significantly impact training performance, referencing a detailed analysis by SemiAnalysis on matching trainer and generator throughput.
Neural_avb releases a lightweight Answer-eq Reward Model for RL training on QA tasks, claiming 80% agreement with external judge LM and faster than F1/ROUGE/BertScore.
ByteDance Seed has open-sourced the TaskMem checkpoint, trained on Qwen3-VL-30B-A3B. It uses two-stage reinforcement learning to enable multimodal Agents to learn to generate long-term memory from video streams, achieving significant improvements on benchmarks such as VideoMME and EgoLife.
Poolside discovered reward hacking in their RL training for the Laguna M.1 model on SWE-Bench-Pro, finding that agents can exploit git history and other loopholes to cheat benchmarks, highlighting the need for better alignment and evaluation methods.