8Planetterraforming/Parameter-golf-v5x

---
license: mit
task_categories:
- text-generation
- text2text-generation
- question-answering
language:
- en
tags:
- parameter-golf
- symbolic-compression
- calibration
- context-state
- exactness
- auxiliary-training
size_categories:
- 10K<n<100K
pretty_name: "Solutions Training V5 Extension"
---

# Solutions Training V5 Extension

## Overview

This dataset is a 40,000-example auxiliary extension to Solutions Training V5.

It is built around one central idea:

**the model should not brute-force, guess, or over-expand when a symbolic transformation is cleaner and lower-entropy.**

This extension was generated primarily from user-provided failure themes:
- very large integers ending in `123`
- cube-volume ×8 scaling rules
- shortcut arithmetic instead of repeated expansion
- asking for missing variables before answering
- keeping one canonical project state
- preserving exact filenames, paths, logs, commands, and delimiters

---

## Core idea

Many models become noisy because they do too much:
- too many speculative continuations
- too much brute-force expansion
- too much stale context
- too much overconfident answering under missing information

This extension teaches the opposite behavior:
- compress instead of expand,
- ask instead of guess,
- preserve the latest verified state,
- keep exact structured strings exact,
- treat huge patterned numbers symbolically.

---

## Why this matters for BPB

If a model expands every structured pattern into long, uncertain continuations, entropy increases.

If a model instead:
- detects patterns,
- compresses them,
- preserves exact suffixes/prefixes,
- and uses short symbolic reasoning,

then it can reduce unnecessary generative drift.

That is the main intuition behind this extension.

---

## Main targeted behaviors

### 1. Symbolic compression over brute-force expansion
Examples teach the model to:
- keep giant structured integers symbolic,
- preserve suffix `123`,
- avoid hallucinating digits,
- use the ×8 cube-volume rule directly,
- use shortcut arithmetic.

### 2. Clarify before answering
Examples teach the model to:
- ask for missing variables,
- separate verified facts from assumptions,
- avoid overconfident advice based on partial context.

### 3. Canonical project state
Examples teach the model to:
- use the newest verified result,
- avoid jumping ahead before the current result is known,
- answer in short, stepwise form.

### 4. Exact strings and artifacts
Examples teach the model to:
- preserve exact filenames,
- preserve exact log names,
- preserve paths, extensions, delimiters, and shell commands.

---

## Splits

- train: 36,000
- validation: 2,000
- test: 2,000

Total: 40,000

---

## Intended usage

This dataset is intended as an **auxiliary extension**, not a replacement for the main official FineWeb training path.

Recommended initial mixing:
- 99% main corpus
- 1% V5 extension

If stable:
- 97% main corpus
- 3% V5 extension

---

## Summary

Solutions Training V5 Extension is designed to reduce:
- guessy continuations,
- stale-context drift,
- brute-force numeric expansion,
- exact-string corruption.

Its purpose is to make the model more symbolic, more compressed, and more exact.