8Planetterraforming/Parameter-golf-v5x

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.