arvindcr4/tinker-rl-bench-checkpoints

TinkerRL-Bench Checkpoint Manifest

A catalogue of every Tinker training run and checkpoint referenced by our NeurIPS paper "A Unified Benchmark for RL Post-Training of Language Models" (repo).

Because Tinker stores weights behind an authenticated tinker://... URI (only the account that ran the training can materialise them), this dataset does not contain the raw .safetensors/archive blobs — it contains the canonical pointer table and full training metadata so anyone with a Tinker API key can fetch the exact artifact that produced a given result.

Contents

File	Rows	Description
training_runs.jsonl	28	One row per training run: training_run_id, experiment, model, model_short, task, seed, rank, lr, group_size, steps, platform, last10_avg, peak_accuracy, last10_accuracy, weight_checkpoints[], sampler_weight_checkpoints[].
checkpoints.jsonl	66	One row per (training_run_id, kind, step): tinker_uri, kind ∈ {weights, sampler_weights}, step, is_final, full joined metadata, source files that reference it.
arithmetic_checkpoints.jsonl, distillation_off_checkpoints.jsonl	—	Original per-step checkpoint indices committed to the repo.
heldout_gsm8k.json	—	Held-out GSM8K evaluation of the top-10 Tinker checkpoints.
all_results_consolidated.json, master_results.json	—	Source of truth for run-level metadata.

Coverage

• 28 unique Tinker training runs across tinker-rl-lab-world-class, tinker-structural-ceiling, tinker-rl-scaling, and skyrl-tinker W&B projects.
• 66 distinct checkpoint URIs: 20 intermediate weights/ (arithmetic
  • distillation-off sweeps with per-20 or per-50 step snapshots) and 45 sampler_weights/ (final + per-step samplers). One run (38d13280...) has 10 mid-training weight snapshots at steps 50–500.
• Models covered include: Llama-3.1-8B-Base/Instruct, Llama-3.2-{1B,3B}, Qwen3-{0.6B, 1.7B, 4B, 8B-Base/Instruct, 14B, 30B-MoE, 32B, 235B}, Qwen3.5-{4B, 27B}, Gemma-2-{2B, 9B}, Nemotron-120B, DeepSeek-V3.1, GPT-OSS-20B, Kimi-K2.

Materialising weights

Tinker weights are downloadable by the training account with:

import os, tinker, urllib.request
sc = tinker.ServiceClient()                    # reads TINKER_API_KEY
rc = sc.create_rest_client()
fut = rc.get_checkpoint_archive_url_from_tinker_path(
    "tinker://<training_run_id>:train:0/sampler_weights/final"
)
url = fut.result().url
urllib.request.urlretrieve(url, "archive.tar")

The returned archive contains LoRA adapters + tokenizer and can be loaded into the corresponding base model with tinker_cookbook.

Companion datasets

• arvindcr4/tinker-rl-bench-wandb — 334 W&B runs + 9,255 history rows from the same experiments.

Citation

@misc{tinkerrlbench2026,
  title   = {A Unified Benchmark for RL Post-Training of Language Models},
  author  = {Arvind, C. R. and Jeyaraj, Sandhya},
  year    = {2026},
  note    = {NeurIPS submission, https://github.com/pes-llm-research/tinker-rl-lab}
}

License

Apache 2.0.