docs: restore dataset card body after normalize re-push

README.md

@@ -61,3 +61,93 @@ dataset_info:
   download_size: 36756684063
   dataset_size: 36752927228
 ---
+
+# FinalMix2
+
+A multi-task **code reinforcement-learning** dataset mixture in the
+[`verl`](https://github.com/volcengine/verl) RL prompt format. It pairs a
+code-generation split with a suite of auxiliary code-understanding tasks so the
+same corpus can drive three training regimes from one repo. It is the
+V3-dedupe successor to `OctoReasoner/FinalMix` (see
+[Relationship to FinalMix (v1)](#relationship-to-finalmix-v1)).
+
+## Splits
+
+| Split | Rows | Contents | Use |
+|-------|-----:|----------|-----|
+| `train_no_aux` | 9,693 | code-generation only | RL on code gen alone |
+| `train_aux_cascade` | 25,538 | all 15,845 auxiliary rows first, then the 9,693 code rows appended (order preserved) | cascade / curriculum RL (aux → code) |
+| `train_aux_multitask` | 25,538 | the same code + aux rows concatenated and shuffled (`seed=42`) | mixed multi-task RL |
+| `validation` | 481 | held-out code-generation problems | eval |
+| `test` | 175 | LiveCodeBench-v6 problems | eval |
+
+The three training splits are built from the **same** underlying rows — they
+differ only in which tasks are included and in what order — so they form a
+controlled three-way comparison:
+
+1. **`train_no_aux`** — code generation only.
+2. **`train_aux_cascade`** — auxiliary tasks then code, for cascade RL.
+3. **`train_aux_multitask`** — code and auxiliary tasks interleaved, for mixed multi-task RL.
+
+```python
+from datasets import load_dataset
+
+code_only  = load_dataset("OctoReasoner/FinalMix2", split="train_no_aux")
+cascade    = load_dataset("OctoReasoner/FinalMix2", split="train_aux_cascade")
+multitask  = load_dataset("OctoReasoner/FinalMix2", split="train_aux_multitask")
+val        = load_dataset("OctoReasoner/FinalMix2", split="validation")
+test       = load_dataset("OctoReasoner/FinalMix2", split="test")
+```
+
+## Code split (9,693)
+
+A more liberal ("V3") deduplication of the source code pools, rebalanced away
+from the contest-heavy v1 mix toward PrimeIntellect:
+
+| Source | Rows | Share |
+|--------|-----:|------:|
+| `code_primeintellect` | 5,241 | 54.1% |
+| `code_contests_o` | 2,538 | 26.2% |
+| `code_taco` | 1,721 | 17.8% |
+| `code_lcbv5` | 193 | 2.0% |
+
+## Auxiliary tasks (15,845)
+
+Twelve `data_source`s spanning ~24 ability sub-tasks that probe code
+understanding beyond generation:
+
+- **Input/output reasoning** — `code_io_taco`, `code_functional_identity`
+  (predict outputs from inputs / inputs from outputs, direct and MCQ).
+- **Complexity** — `code_time_complexity`, `code_space_complexity`,
+  `code_cpu_ranking`, `code_memory_ranking` (predict/rank time, space, CPU, memory).
+- **Security** — `code_sast_cwe` (predict/localize CWE weaknesses).
+- **Retrieval** — `code_crp_retrieval` (`coderpile_retrieval`).
+- **Localization** — `code_change_localization`, `code_var_tracing`
+  (locate edits; trace variable values).
+- **Compilation** — `code_compile_status` (predict whether code compiles).
+- **Instruction following** — `codeif` (verifiable instruction-following, generate & edit).
+
+## Schema
+
+Standard `verl` RL fields:
+
+| Field | Type | Notes |
+|-------|------|-------|
+| `data_source` | string | routes the reward function |
+| `prompt` | list of `{role, content}` | chat-formatted problem |
+| `ability` | string | task category |
+| `reward_model` | struct `{style, extraction_method, ground_truth, key}` | scoring spec |
+| `extra_info` | struct `{id, lower_pass_rate, upper_pass_rate}` | per-example metadata |
+
+Code-generation rows are scored by executing model output against tests in a
+sandbox; auxiliary rows are scored by rule / answer extraction against
+`ground_truth`.
+
+## Relationship to FinalMix (v1)
+
+`FinalMix2` rebuilds the code split of `OctoReasoner/FinalMix` on a more liberal
+dedupe (9,693 code rows vs. 6,000) and rebalances the source distribution — v1
+was `code_contests_o`-dominated (~50%), v2 leads with `code_primeintellect`
+(~54%). The combined training splits grow accordingly (25,538 vs. 22,000). The
+schema, the auxiliary-task set, and the `validation`/`test` eval splits are
+carried over unchanged from v1.