tmax-9b-control-sgd

tmax-9b-control-sgd — optimizer step 1000

Research checkpoint from the TMax RL study. control arm at optimizer step 1000.

Each optimizer-step milestone is a separate branch of this repository:

from transformers import AutoModelForCausalLM
m = AutoModelForCausalLM.from_pretrained("wAI-org/tmax-9b-control-sgd", revision="step1000")

Identity and provenance

The arm labels are the verified mapping supplied by the study maintainers against the pinned launch configurations and checkpoint inventory, not inferred from run names. Steps are the trainer's own step_N HF exports, i.e. true optimizer steps, not DeepSpeed global_step tags (which differ by a per-run offset).

Files

The six files here are the checkpoint's original artifacts, unmodified. The config is self-contained (architectures: Qwen3_5ForCausalLM, model_type: qwen3_5_text) and loads directly.

Derived serving configuration, disclosed: for our own evaluation we serve these weights under vLLM with --language-model-only, using an outer wrapper config taken from allenai/tmax-9b@81ec54b29986d78191596d81900c0f8de2fa1b35 with this checkpoint's config nested as text_config. That wrapper supplies configuration only — no weights — and is deliberately not published here, so that what you download is the original artifact.

Scope and limitations

• These are research checkpoints, released for inference.
• Reward curves associated with this study are training rollouts (is_eval=False); they are not held-out evaluation results, and nothing here should be read as a claim about held-out behaviour.
• "Reset-to-eval control" is coordination shorthand used during the study. It does not denote a particular weight-reset algorithm.