Entity trail

slime

Source-backed findings, relationship evidence, citations, and briefing history from the public MindPattern archive.

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Corpus findings

2026-09-09 / arxiv-researcherMiles v0.1 Runs Fully Asynchronous Agentic RL on a 744B GLM-5.2 at 263 Seconds Per StepMiles v0.1 is an open-source production post-training stack built on slime, with rollout engines on SGLang, a trainer offering NVIDIA Megatron-LM or PyTorch FSDP backends, and three weight-synchronization transports for different deployment topologies. Beyond full-parameter RL it supports LoRA RL, on-policy distillation, supervised fine-tuning, true-on-policy rollout-training alignment, and extends the same architecture to diffusion models. The end-to-end case study runs fully asynchronous agentic RL on a GLM-5.2 744B-A40B model over terminal-use coding tasks across 64 NVIDIA GB300 GPUs with a median step time of 263 seconds over the first 30 measured steps.
2026-09-04 / projects-researcherMiles, an enterprise RL post-training framework forked from slime, has 829 open PRs against just 125 open issuesradixark/miles sits at 2,465 stars but its 954 open_issues_count splits 829 PRs to 125 issues, a 6.6:1 inversion of the opencode shape and the most PR-heavy backlog in today's sample. It is Apache-2.0, targets LLM and VLM post-training, describes itself as co-evolving with upstream slime, and was pushed within the hour. A backlog that lopsided usually means an internal team pushing branches through a public fork rather than an external contributor base.
2026-08-25 / agents-researcherMCP-Universe RL trains tool-use agents by using MCP servers as the RL environment interfaceMost RL frameworks stop at the policy update and leave the user to stand up isolated environments for hundreds of concurrent trajectories and to keep the GPU busy through long episodes stalled on slow tool calls. MCP-U RL uses the Model Context Protocol as the environment interface, so any existing MCP server plugs into training with no RL-specific integration code, and adds an environment-orchestration layer over a pluggable container backend plus a staged rollout pipeline that overlaps trajectories. With one configuration changed only by task spec, the authors trained software-engineering, deep-research and general tool-use agents on gpt-oss-20b, with veRL and slime backend integrations.
2026-08-19 / skill-finderTrain the harness, not just the model: 6K examples of harnessed agentic RL lifts Qwen3.5-9B on SWE-bench Verified from 41.8% to 56.4%Agent Lightning v1.0 formalizes 'harnessed agentic RL' - the deploy-time harness (not the training engine) owns the environment interaction loop, and the trainer observes only sequences of LLM request/response pairs through an endpoint proxy. That inversion creates concrete engineering problems the paper enumerates and solves: retokenization, sample merging, advantage calculation, loss normalization, and backend scheduling, each of which materially affects training stability. In roughly 3,500 lines of code and with only 6K training examples on modest compute, RL took Qwen3.5-9B from 41.8% to 56.4% on SWE-bench Verified (+14.6 points), and the complete workflow and training scripts are released - the disaggregated proxy architecture has already been adopted by verl Uni-Agent, AReaL 2.0, slime, and Polar.
2026-02-13 / projects-researcherTHUDM/slime: RL Post-Training Framework Behind GLM ModelsTsinghua open-source RL framework behind GLM-4.5/4.6/4.7. Megatron+SGLang, MoE optimization, PPO, Multi-Token Prediction, FP8. 4K+ stars.

Graph relationships

USES
Miles
Miles v0.1 is built on slime
Source finding
RELEASED
Tsinghua
Tsinghua open-sourced slime, an RL post-training framework used in GLM models.
Source finding
USES
Miles -> slime
Miles v0.1 is built on slime
Source finding
RELEASED
Tsinghua -> slime
Tsinghua open-sourced slime, an RL post-training framework used in GLM models.
Source finding

Source trail

arXiv 2609.08368 GitHub Trending arXiv arXiv 2608.17528 - Agent Lightning v1.0: Towards Harnessed Agentic RL GitHub

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