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mr9b-layeraudit-8node-215

Transformers
Safetensors
meta-reasoning
rl
grpo
qwen3_5
layer-audit
research-checkpoint
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metadata 
license: other
library_name: transformers
tags:
  - meta-reasoning
  - rl
  - grpo
  - qwen3_5
  - layer-audit
  - research-checkpoint

mr9b meta-reasoning RL — layer-audit — 8-node/215 checkpoints

Mid-training research checkpoints from a fully-async GRPO run with the pipelined frozen-E layer audit enabled (meta_reasoning.layer_audit.enable=true).

  • Base: Qwen3.5 (~9B hybrid, model_type: qwen3_5, 32 layers)
  • Method: fully-async GRPO meta-reasoning RL (VERL fully_async_policy)
  • Layer audit: after each completed reasoning layer, a frozen executor audits it (classify explorations as new/refinement/verification/duplicate, diagnose tunnel vision + gaps) and injects one-step control feedback into the next MR step.
  • Topology 215: 2 trainer / 1 rollout / 5 frozen-E nodes (8 nodes, TP8/PP1/DP2)
  • Experiment: mr9b_layeraudit_8node_215_20260718_231300
  • W&B project: meta_reason_rl_h100_5node

Contents

Each subfolder is a standalone HF model (weights + tokenizer + chat template):

  • global_step_68/
  • global_step_69/
from transformers import AutoModelForCausalLM, AutoTokenizer
m = AutoModelForCausalLM.from_pretrained("aviralku/mr9b-layeraudit-8node-215", subfolder="global_step_69")
t = AutoTokenizer.from_pretrained("aviralku/mr9b-layeraudit-8node-215", subfolder="global_step_69")

Private research checkpoints; not an official release.