open-sci-ref-1.7b-mixturevitae-wo_ot3-300B-4096-longsft_16k-SFT-Tulu3-decontaminated

Tulu3 SFT on MixtureVitae wo_ot3 300B (16k), for OT3 Phase-2 post-training#

Recipe#

Standard Tulu3 recipe (SFT then DPO), same hyperparameters used throughout this study (matches configs/trl/sft-mxv.yaml in OpenEuroLLM/post-training, and recipes/dpo/generate_jupiter.py in alignment-handbook for DPO, which has no post-training-repo equivalent).

• SFT: lr 5e-6, linear schedule, 2 epochs, warmup 0.03, effective batch 128 (16 x 4 GPU x 2 nodes), bf16, seed 42, max_length 4096. Dataset: ali-elganzory/tulu-3-sft-mixture-decontaminated.
• DPO: lr 5e-7, linear schedule, 1 epoch, warmup 0.1, beta 5, effective batch 128 (32 x 4 GPU x 1 node), bf16, seed 42, max_length 2048. Starts from the SFT checkpoint. Dataset: ali-elganzory/llama-3.1-tulu-3-8b-preference-mixture-decontaminated.

Deviation from the reference config: attn_implementation: sdpa instead of flash_attention_2 (no aarch64 flash-attn wheel on JUPITER).

Base model#

laion/open-sci-ref-1.7b-mixturevitae-wo_ot3-300B-4096-longsft_16k -- MixtureVitae v1 decontaminated with only OpenThoughts3 removed (93 of 96 shards), 300B tokens, extended to 16k context. Produced for the NeurIPS 2026 rebuttal of Strong Post-Training from Permissive, Reasoning-Dominant, Web-Scale Pretraining, to test whether OpenThoughts3 is critical for reasoning-boosting post-training at the paper's main 300B pretraining scale.

Training signal#

Reported per-step (not the summary train_loss, which is distorted by resuming training across separate SLURM job submissions and only reflects the final micro-segment).

• 14,634 / 14,634 steps (2.0 epochs). Converged loss ~0.92-1.0, mean token accuracy ~0.76.