asingh15/prm-sft-polaris-mc
PRM-SFT Polaris MC — Monte-Carlo value (unfiltered) Soft-target process reward model (PRM) training data with per-prefix Monte-Carlo values, in the style of Math-Shepherd. Each row is one prefix of a reasoning trace on a Polaris math problem, labeled with V(prefix)=P(correct∣prefix)=#correct continuations#continuationsV(\text{prefix}) = P(\text{correct} \mid \text{prefix}) = \frac{\#\text{correct… See the full description on the dataset page: https://huggingface.co/datasets/asingh15/prm-sft-polaris-mc.
PRM-SFT Polaris MC — Monte-Carlo value (unfiltered)
Soft-target process reward model (PRM) training data with per-prefix Monte-Carlo values, in the style of Math-Shepherd. Each row is one prefix of a reasoning trace on a Polaris math problem, labeled with
$$V(\text{prefix}) = P(\text{correct} \mid \text{prefix}) = \frac{\#\text{correct continuations}}{\#\text{continuations}}$$
estimated from N shared-prefix continuations sampled by Qwen3.5-4B.
This is the unfiltered set: every prefix is kept, including the fully-collapsed V=0 and V=1 prefixes. It is not filtered to mid-difficulty problems or to the uncertain V∈[0.1,0.9] band.
Splits
Value distribution (train)
Mean V = 0.737. The mass at 0/1 reflects label collapse on problems the base model reliably solves or reliably fails — the reason value-filtered variants exist.
Columns
Intended training use
One row per prefix with a placeholder yes verdict token. The soft-target PRM loss reads reward as V and minimizes soft cross-entropy at the verdict position,
$$-\big[\,V \log P(\text{yes}) + (1-V)\log P(\text{no})\,\big],$$
so P(yes) regresses to V. Same schema as the outcome-PRM sets, so it is concatenable.
Provenance
Branched-rollout pipeline: build branch set (prefixes) → sample N shared-prefix continuations → grade (math_verify + LLM-judge relabel) → aggregate to per-prefix V. Correctness comes from the relabeled grades (the raw branch shard is largely ungraded).