guido-0.5b-grpo-step380

Guido 0.5B post-GRPO, step 380

This repository preserves the exact no-TIR, no-Muon, BETA=0 Guido checkpoint from Slurm job 48981274 (grpo_is_cos500). It is the checkpoint used in the reported CP4-versus-post-GRPO evaluations. It must not be confused with the later Muon runs or the legacy GRPO checkpoints 2562 and 2566.

Canonical files

• checkpoints/step_000380.pt: inference policy, an Aplos/PyTorch state dict.
• checkpoints/step_000380.train.pt: complete resume state, including optimizer state and FP32 master weights.
• checkpoints/step_000380.json: machine-readable snapshot metadata.
• reproduction/train_grpo_step380.py: exact trainer used by job 48981274.
• reproduction/job_48981274.sbatch: exact Slurm submission script.
• logs/grpo_is_cos500_g16lg32_4n_48981274.out and .err: complete job logs.
• evals/final_cp4_vs_p380/: final three-seed evaluation tables and protocol.

Integrity

The source checkpoint actually loaded by the run was BossCalabrese/guido-sft-10b/checkpoints/sft_final.pt, SHA-256 6e2685fa0942bda88e17aa7ef023dc59d4bac87cadfe4d310810077fbfaee5bc.

Snapshot state

• rollout batch: 380
• optimizer step: 760
• usable groups: 12160 / 16000
• cumulative raw reward: 0.5026031199758842
• batch raw reward: 0.5872395833333334
• batch usable-group reward: 0.515625
• current policy LR: about 4.2e-6

The overall job later failed after batch 383, so this repository deliberately uses the durable step-380 snapshot that all post-GRPO evaluations referenced.

Training configuration

The run used 4 Leonardo nodes, 4 A100 GPUs per node (world_size=16):

Other provenance:

• data: Guido-GRPO-pipeline/grpo_pool_FINAL.jsonl, SHA-256 604cbe4a1fc34e67814a399ab6284e47b90c1941913a6a761bdd83539bf0c18b
• Aplos git commit: 47492b2
• Aplos Vathos/blocks.py SHA-256: 2b3a9029516da1edec835817003b978b89d15a6ce229f5a68ad1b799c3a0fcb3
• Aplos Vathos/functions.py SHA-256: 6beda892946f9a013719563ab324788f8c808d2ffa037c32bdf6448d75857179
• the loader reported missing=0, unexpected=0, and patched all 24 FFN blocks to SiLU.

Final sampled evaluation

Protocol: 16 samples per problem, temperature 0.6, top-p 0.95, no top-k, 2048 generated tokens, prompt cap 512, compiled decoding, seeds 1234/2027/3407. Values below are mean +/- sample standard deviation across seeds.

The complete tables, paired CP4 deltas, settings, and raw evaluation material are also preserved in the private dataset repository BossCalabrese/Guido-GRPO-pipeline.

Loading

The policy file is a plain ordered PyTorch state dict:

import torch

state_dict = torch.load("checkpoints/step_000380.pt", map_location="cpu")
model.load_state_dict(state_dict, strict=True)

Use the exact trainer and Aplos commit recorded above for training resume or strict reproduction. step_000380.train.pt is not required for inference.