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laion/jtd-d5-69-30B
jtd-d5-69-30B is a reinforcement learning model from laion. Use it for the reinforcement learning task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as apache-2.0.
GSPO checkpoint (step 69; the run closed at step 73 of 80 when its restart chain was sealed at close-out — the final links ran through the 2026-08-28 GPFS/Daytona platform degradation) from the Jupiter TaskTrove DAPO…
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From the Hugging Face model README
GSPO checkpoint (step 69; the run closed at step 73 of 80 when its restart chain was sealed at
close-out — the final links ran through the 2026-08-28 GPFS/Daytona platform degradation) from the
Jupiter TaskTrove DAPO campaign, arm D5 — the D0 plain-GRPO control with the objective swapped
to GSPO: policy_loss_type: gspo, eps 3e-4/4e-4 (paper range), loss_reduction: sequence_mean,
use_tis: false, no dynamic sampling, no KL.
laion/codeforces-v2 (10,000 competitive-programming tasks, sandboxed agent
rollouts via Harbor/Daytona, terminus-2 harness, FSDP2 fully-async trainer on 16x GH200)reward/avg_raw_reward (0.2784 at step 69 vs 0.2481
at step 72; the same step wins on EMA of reward/avg_pass_at_8, 0.4845 vs 0.4723)rl_config.yaml in this repotraining_logs/ in this repo (per-link metrics CSVs, trial results, report,
all 17 job logs + the checkpoint-export job log)Training-time Daytona/Harbor rollouts: open-athena/jtd-d5
(the last episode of each trial — the rollouts the policy trained on after rollback/truncation).
This trace dataset conserves a deterministic 25% subsample of the full trial set (17,722 of
70,886 trials; every 4th trial of the sorted list) = 15,880 rows / 93 shards, per a
storage-conservation decision at cleanup time.