laion/tasktrove-dq-pymethods2test-step75-30b-a3b

tasktrove-dq-pymethods2test (step 75)

RL checkpoint from the TaskTrove data-quality sweep, trained with SkyRL from Qwen/Qwen3-Coder-30B-A3B-Instruct using RLOO over agentic software-engineering tasks executed by OpenCode in sandboxed environments.

• Base model: Qwen/Qwen3-Coder-30B-A3B-Instruct (Qwen3 MoE, 48 layers)
• Checkpoint: global_step_75
• Source run: rl-tasktrove-dq-sweep-30b-terminus2-qwen-20260725-231748-4fe0b4
• Weights: 16 safetensors shards, 61.1 GB

What this is for

The sweep measures dataset quality, not model quality. Each arm trains the same base model on a different TaskTrove source so the sources can be compared. These checkpoints are research artifacts for that comparison. None has been evaluated as a general-purpose model, and no benchmark numbers are claimed here.

Training configuration

RLOO (advantage_estimator: rloo_n) with megatron backend, tensor-parallel 4, pipeline-parallel 2, expert-parallel 4, across 32 H100s. The objective is deliberately unregularized: use_kl_loss: false, use_entropy_loss: false, and policy_update_steps: 1, which leaves the PPO clip ratio inert at 0.0. That choice makes entropy dynamics the primary failure mode across the sweep, and it is why several arms ended early.

Provenance

Exported from a torch.distributed.checkpoint megatron checkpoint by re-running the trainer's own export path (bridge.save_hf_weights) at the checkpoint's own step, so no offline conversion was involved. Shard count, index total_size and weight_map completeness were verified against the object store before upload.

Training Traces

Training-time OpenCode/Harbor rollouts for this run are published as a companion dataset: laion/terminal_bench_2_tasktrove_dq_pymethods2test_step75_30b_a3b_20260730_054052

The dataset contains the last episode of each trial (per make_and_upload_trace_dataset --episodes last) — the rollouts the policy was trained on.

Training Logs

training_logs/ holds the retained trainer log for this run, and it is partial. The Iris log server retains only the run's first attempt (2026-07-25 23:20 to 07-26 09:48 UTC), during which the run resumed at global_step_29 and completed one optimizer step before stalling in generation. The job was preempted four times; the attempts that trained steps 30 through 75 left no retrievable log, on the log server or in the object store's archived Ray sessions.

Read training_logs/COVERAGE.md before using anything in that directory. metrics.csv has a single row and is not a learning curve. No metric trajectory exists for the span that produced these weights.