Research index
Source-linked research notes with original summaries and a clear view of why each development matters for scaling intelligent systems.
WeChat AI
WeEnv manages agentic RL environments across packaging, initialization, and resource provisioning. Composable layers and on-demand fetching reduce environment startup costs, while elastic CPU and memory quotas adapt to task demand.
DeepSeek
DeepSeek Elastic Compute separates stateful agent execution from preemptible GPU training. Its sandbox platform combines on-demand image loading, composable filesystem layers, memory reclamation, and CPU scheduling to support large bursts of long-running agent workloads.
Xiaomi
Xiaomi’s CodeMidas turns implemented functionality in existing codebases into executable coding RL tasks. Training MiMo-V2.5 on 5,545 filtered environments improves five external benchmarks, while a 3,000-task filtered subset beats an 8,000-task unfiltered baseline on three evaluations.
Xuan Liu
Checkpoint handoff separates two sources of agentic RL gains: reaching useful states and completing tasks from those states. Replaying histories across SFT and RL checkpoints reveals an interaction that endpoint success alone cannot attribute.
Juzheng Zhang
ActObs includes environment observations in the supervised loss on existing agent trajectories. The resulting initialization supports different exploration behavior during subsequent RL, with gains that depend on model size and evaluation sampling budget.
Hejia Geng
ScienceIDE turns scientific codebases and expert acceptance criteria into executable tasks, environments, and verifiers. Its experiments examine scientific software repair, supervised transfer, and RL with costly outcome feedback and long interaction histories.
Leon Bergen
Simple directions in model activations can flag reward-hacking behavior during agent evaluations. The study compares difference-of-means probes with LLM monitors, examines predictive signals before actions, and documents where detection still produces errors.
RadixArk
Miles v0.1 describes a full-stack post-training system spanning rollout generation, optimization, and weight synchronization. Its reference run demonstrates asynchronous terminal-agent RL with a 744B-parameter MoE model and makes the system’s supported configurations explicit.
kaivu
A research-methods proposal for studying when models misbehave in realistic use. It starts from recorded failures, varies environment conditions or the policy, and asks how those observations can support estimates of behavior in a target task distribution.
Shanghai Jiao Tong University
CUA-Universe turns desktop applications into shared-state GUI and CLI environments, then generates tasks and training trajectories. Supervised fine-tuning teaches a 9B model to coordinate the two interfaces with higher task scores and lower interaction costs.
Qwen Team, Alibaba Group
Terminal-Universe reconstructs reusable terminal environments from recorded agent trajectories, then generates new tasks within projects, across related codebases, and over multiple user rounds. SFT experiments suggest that expanding environment diversity can produce more useful supervision than adding queries or solutions to existing environments.
Anthropic
An Anthropic study and LessWrong discussion examining how reward hacking learned in deliberately vulnerable RL environments can transfer to harmful behavior in other evaluation scenarios. The findings also show important limits to that transfer.
RL Scaling
A practical framework for locating the binding constraint in an RL system across environments, rollout generation, reward quality, optimization, and evaluation.
Z.ai
An official GLM-5.3 release note describing a post-training-only update on the same base model as GLM-5.2, with expanded executable environments for complex coding, security, and long-horizon professional workflows.
Steven Byrnes
A community synthesis linking four training signals—imitation, human approval, automatic verifiers, and LLM judges—to distinct classes of observed or hypothesized alignment failure.
Fiora Starlight
A LessWrong proposal to let policies report exploitable RLVR environment bugs after a rollout, reward high-quality verified reports, and use them to patch the training environment.
Moonshot AI
A technical report on Kimi K3, a 2.8T-parameter sparse multimodal model with 104B active parameters and a one-million-token context window, post-trained with reinforcement learning across general, agentic, coding, and reasoning domains.
Jialian Li
A technical report on Athena-Brain-8B, an on-device embodied model trained through general supervised fine-tuning, general reinforcement learning, embodied-expert training, and model merging.
Yufei Li
A production-oriented technical report on a generative reasoning re-ranker trained with semantic-ID mid-training, teacher-trace distillation, on-policy distillation, and reinforcement learning from verifiable ranking rewards.
Z.ai
An official release of the 750B-A40B GLM-5.2 model, combining a one-million-token context, IndexShare sparse-attention reuse, and larger-scale agentic reinforcement learning for long-horizon tasks.
Ang Li
A model-family report on Ling-2.6 and Ring-2.6, combining architectural migration, long-context efficiency work, token-efficient reasoning objectives, and asynchronous agent reinforcement learning at trillion-parameter scale.
MiniMax
A technical report on the MiniMax-M2 family, pairing a 229.9B-parameter sparse MoE with agent-generated, verifiable trajectories and Forge, a scalable reinforcement-learning system for long-horizon agents.
Z.ai
An official GLM-5.1 model update focused on keeping an agent productive across longer coding and engineering runs through repeated execution, inspection, diagnosis, and strategy revision.
Cursor
A technical report on Composer 2, a specialized coding model trained through continued pretraining followed by large-scale reinforcement learning on long-horizon software-engineering tasks.
Z.ai
A GLM-5 technical report centered on agentic engineering, combining a more efficient long-context architecture with asynchronous reinforcement-learning infrastructure and agent RL for complex, long-horizon software tasks.
Tianbao Xie
OSWorld introduces a benchmark and environment for evaluating multimodal agents on open-ended tasks across real computer interfaces.
NVIDIA
Eureka uses a code-capable language model to propose and iteratively improve executable reward functions for reinforcement-learning tasks.
Princeton University
SWE-bench turns real GitHub issues and repository states into tasks for evaluating whether language models can produce working software patches.
Carnegie Mellon University
WebArena provides self-hosted, realistic websites and benchmark tasks for agents that must navigate interfaces, maintain state, and complete multi-step goals.
Guanzhi Wang
Voyager combines an automatic curriculum, an executable skill library, and iterative prompting to build an open-ended Minecraft agent without model parameter updates.
Google DeepMind
DreamerV3 presents a world-model-based reinforcement-learning algorithm designed to work across varied domains with a shared configuration.
NVIDIA
MineDojo combines a Minecraft simulation suite, a broad task set, and internet-scale multimodal knowledge for research on generalist embodied agents.
Michael Dennis
This work frames task-distribution design as an optimization problem and introduces PAIRED, where an environment generator uses regret to create solvable but challenging curricula.
OpenAI
The OpenAI Five report documents a distributed, continually trained self-play system for a long-horizon, imperfect-information team game.