| --- |
| license: apache-2.0 |
| task_categories: |
| - text-generation |
| - reinforcement-learning |
| tags: |
| - tinker |
| - grpo |
| - ppo |
| - rlhf |
| - checkpoints |
| - manifest |
| pretty_name: TinkerRL-Bench Checkpoint Manifest |
| size_categories: |
| - n<1K |
| configs: |
| - config_name: training_runs |
| data_files: training_runs.jsonl |
| - config_name: checkpoints |
| data_files: checkpoints.jsonl |
| --- |
| |
| # 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](https://github.com/pes-llm-research/tinker-rl-lab)). |
|
|
| 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: |
|
|
| ```python |
| 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`](https://huggingface.co/datasets/arvindcr4/tinker-rl-bench-wandb) |
| — 334 W&B runs + 9,255 history rows from the same experiments. |
|
|
| ## Citation |
|
|
| ```bibtex |
| @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. |
|
|
