---
license: mit
task_categories:
- text-generation
language:
- en
tags:
- collabllm
- multiturn
- rl
- grpo
size_categories:
- 1K<n<10K
configs:
- config_name: default
  data_files:
  - split: train
    path: rl_train.parquet
  - split: validation
    path: rl_validation.parquet
---

# CollabLLM medium — RL (GRPO) train/validation split

Inputs for GRPO training on the CollabLLM **medium** document-writing task, as used to produce
the `verl-grpo-medium-qwen3-4b-step{50,100,129}` checkpoints.

| file | rows | size |
|---|---|---|
| `rl_train.parquet` | 2072 | 16M |
| `rl_validation.parquet` | — | 2.2M |

Derived from the CollabLLM medium task (source articles: `Kamaljp/medium_articles`). Each row is
a single-turn prompt that the training loop expands into a multi-turn conversation with a
simulated user, then scores with an LLM judge.

## ⚠️ Known issue: train/eval overlap causes label leakage through the reward

**Measured, not suspected.** 35 of the 100 medium test prompts also appear in `rl_train.parquet`,
and on those rows the BLEU reward target is **byte-identical** to the test reference document.

Why this matters more for RL than for SFT: in supervised fine-tuning the reference never enters
the training text, so an overlap is only topical leakage. In RL the reference **is** the reward
target for the `bleu_score` metric, so an overlapping row leaks the eval label directly through
the reward signal.

Root cause: the upstream release sampled the example pool *before* applying its own train/test
split, so the split does not separate them.

**If you train on this, hold out the overlapping prompts, or report numbers on the
non-overlapping 65.** The overlap is a property of this file and travels with it.

## Reproducing

The reward is a weighted sum of `bleu_score` (+1), `interactivity` (+1) and `token_amount`
(−0.1), with the judge and user simulator served as separate vLLM endpoints. Reference three-role
setting, thinking **off** everywhere:

| role | model |
|---|---|
| assistant (trained) | `Qwen/Qwen3-4B` |
| user simulator | `Qwen/Qwen3.5-9B` |
| judge / reward | `Qwen/Qwen3.5-27B` |

Full setup and the acceptance tests (5-step training replay, `val@0` = 1.0618) are in
`studynotes/new_cluster_onboarding.md` in the code repo.

## Note on the checkpoints

Training peaked at **step 100** and regressed afterwards: step 129 has flat BLEU and
significantly worse interactivity. Report step 100. Do not pick a checkpoint on verl's
`critic/rewards/mean` — it credits brevity, which the benchmark judge penalises.