FinalMix2

A multi-task code reinforcement-learning dataset mixture in the 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)).

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.

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_sources 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.