| --- |
| license: mit |
| language: |
| - en |
| pretty_name: ChokeBench v1 |
| size_categories: |
| - n<1K |
| source_datasets: |
| - Qwen/ProcessBench |
| tags: |
| - reasoning |
| - chain-of-thought |
| - process-reward-model |
| - verifier |
| - llm-evaluation |
| - benchmark |
| - math |
| configs: |
| - config_name: default |
| data_files: chokebench_v1.jsonl |
| --- |
| |
| # ChokeBench v1 |
|
|
| **Does your verifier find the step where a reasoning failure is actually decided?** |
|
|
| ProcessBench grades a verifier on finding the *human-labelled first error*. ChokeBench also asks it to find the |
| **choke point**: the step where the generator's own chance of success collapses. Ground truth comes from |
| resampling the generator 64 times from every prefix of every failed trace, so it measures what each step is |
| worth as a place to restart from, not what a human annotator thought was wrong. |
|
|
| Headline finding: a process reward model finds **73%** of human first errors but only **45%** of choke points, |
| and the human label itself is the choke point only about **half** the time. |
|
|
| - Paper and code: [github.com/HarshDubey23/early-failure-detection-cot](https://github.com/HarshDubey23/early-failure-detection-cot) |
| - Authors: Harsh Dubey, Harshit Saini, Prashant Jain, Himanshu Kumar, Gaurav Kaushal, Rohit Chaudhary |
| (Dept. of CSE (AI&ML), KIET Group of Institutions) |
|
|
| ## What is in it |
|
|
| 469 failed maths traces from [ProcessBench](https://huggingface.co/datasets/Qwen/ProcessBench), each run on one |
| of four generators: Qwen2.5-1.5B-Instruct, Qwen2-1.5B-Instruct, Qwen2.5-Math-7B-Instruct and Llama-3.1-8B-Instruct. |
| Scoring uses the open ones (not capability-limited, prefix-0 success rate above 1/8). |
|
|
| | Field | Meaning | |
| |---|---| |
| | `trace_id` | ProcessBench item, e.g. `processbench:gsm8k:gsm8k-156` | |
| | `generator` | model that produced the rollouts | |
| | `n_steps` | steps in the trace | |
| | `human_first_error` | 1-based step of the expert-labelled first error | |
| | `successes`, `continuations` | per prefix k = 0..n-1: correct continuations out of those sampled | |
| | `mean_continuation_tokens` | per prefix: mean tokens generated from there | |
| | `prm` | per step: Qwen2.5-Math-PRM-7B score | |
| | `capability_limited` | true if the generator rarely solves the problem even from scratch | |
|
|
| No problem text is included; join on `trace_id` with ProcessBench if you need it. |
|
|
| ## Grade your own localiser |
|
|
| Name one step per trace and score it with [`chokebench_score.py`](chokebench_score.py) (NumPy only, runs in seconds): |
|
|
| ```bash |
| python chokebench_score.py chokebench_v1.jsonl submission.json # {"<trace_id>|<generator>": step, ...} |
| ``` |
|
|
| It reports how often you hit the human first error, the posterior probability that you hit the choke point |
| (Jeffreys posterior on every prefix's success rate, so sampling noise is not counted as a miss), and the gain of |
| resampling from your step versus a plain restart. This scorer skips the paper's sample-split weighting, so its |
| numbers sit within about 0.01 of the official ones below. |
|
|
| ## Official results (paper protocol, n = 230, 95% bootstrap CIs) |
|
|
| | Localiser | finds human first error | finds choke point | gain vs restart, per attempt | |
| |---|---|---|---| |
| | Human first error | 1.00 | 0.51 [0.45, 0.56] | -0.066 | |
| | PRM: largest drop | 0.73 | 0.45 [0.39, 0.51] | -0.103 | |
| | PRM: first step < 0.5 | 0.68 | 0.38 [0.32, 0.44] | -0.077 | |
| | PRM: minimum | 0.61 | 0.39 [0.34, 0.45] | -0.158 | |
| | Learned (nested CV, held-out model) | 0.68 | 0.39 [0.33, 0.44] | -0.085 | |
| | Random step | 0.18 | 0.18 | -0.224 | |
| | First step (= restart) | 0.14 | 0.28 | 0 | |
|
|
| Even an oracle that names the choke point for free gains only +0.028 over restart at a four-attempt budget. |
|
|
| ## Licence |
|
|
| MIT. Derived from ProcessBench (Apache-2.0); the generator and PRM outputs follow their model licences |
| (Qwen, Llama 3.1 Community License). |
|
|
| ## Citation |
|
|
| ```bibtex |
| @misc{dubey2026chokepoints, |
| title = {Errors Are Not Choke Points: Counterfactual Localisation of {LLM} Reasoning |
| Failures, and Why Restart Is Hard to Beat}, |
| author = {Dubey, Harsh and Saini, Harshit and Jain, Prashant and Kumar, Himanshu and |
| Kaushal, Gaurav and Chaudhary, Rohit}, |
| year = {2026}, |
| howpublished = {\url{https://github.com/HarshDubey23/early-failure-detection-cot}} |
| } |
| ``` |
|
|
