How do I install the G2rl Gradient Guided skill?

Run npx skillmds@latest add adu2021/g2rl-gradient-guided in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is G2rl Gradient Guided safe to use?

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Which AI agents work with G2rl Gradient Guided?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is G2rl Gradient Guided free to use?

Yes. Installing skills from SkillMD is free. This skill is licensed under MIT.

Who published G2rl Gradient Guided?

adu2021 (@adu2021) published this skill. Their other Agent Skills are listed on their SkillMD profile.

G2rl Gradient Guided by adu2021 — Agent Skill

Name: G2rl Gradient Guided
Author: adu2021

G2rl Gradient Guided

Guide LLM exploration through the model's own gradient geometry rather than external signals. Extract sequence-level gradient features measuring how tokens would reshape output distributions. Reward responses introducing novel gradient directions while deemphasizing redundant ones. Achieve orthogonal gradient directions and improved accuracy.

adu2021 c2283b8 3.5 KB Updated Sep 17, 2026

File contents

Skill Summary

G2RL introduces gradient-guided reinforcement learning that leverages the model's own gradient geometry for exploration guidance. Rather than external signals like entropy or semantic embeddings, the method computes sequence-level features from final-layer gradient sensitivity, comparing responses by how differently they would update policy parameters. Correct but geometrically redundant answers are slightly downweighted; correct answers with novel gradient directions receive bonuses. The approach achieves "self-guided" exploration: the model learns which variations meaningfully reshape its own parameters.

When To Use

Training language models with exploration that reflects actual policy update dynamics
Scenarios where external exploration signals misalign with meaningful parameter updates
Projects exploring policy gradient interpretability and update geometry
Research on self-guided exploration in reinforcement learning

When NOT To Use

Scenarios where external exploration signals (entropy, diversity) already work well
Real-time applications where gradient feature computation adds overhead
Domains where exploration doesn't benefit from update-direction alignment
Models with strict computational budgets prohibiting additional calculations

Core Technique

Three key components enable gradient-guided exploration:

1. Gradient Feature Extraction For each response token, extract "sequence-level feature" measuring how that token would reshape the model's output distribution. Compute the token's first-order sensitivity—how strongly the token would affect final predictions through gradient updates.

2. Exploration Scoring Compare responses within a group based on how differently they would update policy parameters. Those introducing novel gradient directions receive reward bonuses, while redundant ones are deemphasized. This measures orthogonality of gradient directions.

3. Reward Shaping Apply bounded multiplicative factor adjusting rewards asymmetrically:

Correct but geometrically redundant answers: slightly downweighted
Correct answers with novel gradient directions: boosted

The asymmetry encourages exploration of underrepresented gradient regions without discouraging correctness.

Key Insight

"Exploration often becomes diffuse, misaligned, or fragile" when driven by external signals that don't reflect the policy's actual update dynamics. G2RL achieves "self-guided" exploration where the model learns which variations meaningfully reshape its own parameters, improving accuracy across math and reasoning benchmarks.

Implementation Notes

Compute gradient features via final-layer gradient sensitivity during forward passes. Group responses by prompt. Compute pairwise gradient direction similarity/orthogonality. Score based on novel gradient directions. Apply asymmetric reward shaping. Fine-tune model with shaped rewards via policy gradient.

References

Original paper: Can LLMs Guide Their Own Exploration? (Dec 2025)
Policy gradient interpretation and geometry
Exploration in reinforcement learning

---name: g2rl-gradient-guideddescription: Guide LLM exploration through the model's own gradient geometry rather than external signals. Extract sequence-level gradient features measuring how tokens would reshape output distributions. Reward responses introducing novel gradient directions while deemphasizing redundant ones. Achieve orthogonal gradient directions and improved accuracy.license: MIT---## Skill SummaryG2RL introduces gradient-guided reinforcement learning that leverages the model's own gradient geometry for exploration guidance. Rather than external signals like entropy or semantic embeddings, the method computes sequence-level features from final-layer gradient sensitivity, comparing responses by how differently they would update policy parameters. Correct but geometrically redundant answers are slightly downweighted; correct answers with novel gradient directions receive bonuses. The approach achieves "self-guided" exploration: the model learns which variations meaningfully reshape its own parameters.## When To Use- Training language models with exploration that reflects actual policy update dynamics- Scenarios where external exploration signals misalign with meaningful parameter updates- Projects exploring policy gradient interpretability and update geometry- Research on self-guided exploration in reinforcement learning## When NOT To Use- Scenarios where external exploration signals (entropy, diversity) already work well- Real-time applications where gradient feature computation adds overhead- Domains where exploration doesn't benefit from update-direction alignment- Models with strict computational budgets prohibiting additional calculations## Core TechniqueThree key components enable gradient-guided exploration:**1. Gradient Feature Extraction**For each response token, extract "sequence-level feature" measuring how that token would reshape the model's output distribution. Compute the token's first-order sensitivity—how strongly the token would affect final predictions through gradient updates.**2. Exploration Scoring**Compare responses within a group based on how differently they would update policy parameters. Those introducing novel gradient directions receive reward bonuses, while redundant ones are deemphasized. This measures orthogonality of gradient directions.**3. Reward Shaping**Apply bounded multiplicative factor adjusting rewards asymmetrically:- Correct but geometrically redundant answers: slightly downweighted- Correct answers with novel gradient directions: boostedThe asymmetry encourages exploration of underrepresented gradient regions without discouraging correctness.## Key Insight"Exploration often becomes diffuse, misaligned, or fragile" when driven by external signals that don't reflect the policy's actual update dynamics. G2RL achieves "self-guided" exploration where the model learns which variations meaningfully reshape its own parameters, improving accuracy across math and reasoning benchmarks.## Implementation NotesCompute gradient features via final-layer gradient sensitivity during forward passes. Group responses by prompt. Compute pairwise gradient direction similarity/orthogonality. Score based on novel gradient directions. Apply asymmetric reward shaping. Fine-tune model with shaped rewards via policy gradient.## References- Original paper: Can LLMs Guide Their Own Exploration? (Dec 2025)- Policy gradient interpretation and geometry- Exploration in reinforcement learning

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