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Motif: Intrinsic Motivation from Artificial Intelligence Feedback
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Motif elicits preferences from Llama 2 70B over pairs of NetHack message captions, distills them into an intrinsic reward model, and trains PPO agents on it, alone or combined with the game score.
Problem
Agents exploring rich, open-ended environments without prior knowledge struggle to know which events are desirable; sparse rewards make learning hard, and LLM knowledge is hard to ground in low-level observation and action spaces.
Approach
Use an LLM as an annotator of preferences over pairs of event captions (not trajectories or actions), distill these preferences into a scalar intrinsic reward over observations, and train RL agents with it; the LLM's anticipatory judgments act as reward-crafting that eases credit assignment.
Key results
- An agent trained only on Motif's intrinsic reward achieves a higher NetHack game score than an agent trained directly on the game score (CDGPT5).
- Combining Motif intrinsic and extrinsic rewards significantly outperforms RND and extrinsic-only baselines and achieves about 30% success on the oracle task, where no prior approach progressed without demonstrations.
- Downstream success on staircase (level 3) increases with LLM size (Llama 2 7B, 13B, 70B); steering prompts yield +106% gold, +17% descents, and +150% kills relative to the zero-knowledge prompt.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Train with Motif's intrinsic reward instead of environment score reward | NetHack score agent · score | game score | Helped |
| Combine Motif's intrinsic reward with environment reward | NetHack agent · score | game score | Helped |
| Use Motif's intrinsic reward | NetHack agent · staircase | success rate | Mixed |
| Use Motif's intrinsic reward, alone or combined with extrinsic reward | NetHack agent · staircase (level 3) and staircase (level 4) | success rate | Helped |
| Combine Motif's intrinsic reward with environment reward | NetHack agent · oracle | success rate: about 30% | Helped |
| Increase the LLM annotator size (ablation) | Motif · staircase (level 3) | success rate | Helped |
| Add task-relevant information to the zero-knowledge prompt (ablation) | Motif · score | game score | Helped |
| Replace the default prompt with a semantically similar reworded prompt (ablation) | Motif · score | game score | No clear effect |
| Replace the default prompt with a semantically similar reworded prompt (ablation) | Motif + extrinsic reward · oracle | behavior toward reaching the oracle | Helped |
| Use a task-specific prompt modifier (ablation) | Motif · Gold collected | gold collected: +106% more gold (64%, 157%) | Helped |
| Use a task-specific prompt modifier (ablation) | Motif · Dungeon descents | descents: +17% more descents (9%, 26%) | Helped |
| Use a task-specific prompt modifier (ablation) | Motif · Monsters killed | kills: +150% more kills (140%, 161%) | Helped |
Surprising findings
- Optimizing only the intrinsic reward outperforms optimizing the game score directly on NetHack score.
- Misalignment by composition: combining aligned intrinsic reward with the task reward led the agent to hack the oracle task by hallucinating rather than descending the dungeon.
Limitations
- Relies on a 70B LLM and extensive prompting; annotation cost and LLM bias toward longer messages.
- Reward uses only the message caption, ignoring other observation components.
- Misalignment by composition can cause reward hacking when combined with task reward.
Experimental setup
Datasets: NetHack Learning Environment gameplay observations (collected with CDGPT5 across 10 seeds) LLM-annotated preference pairs over message captions
Benchmarks: NetHack Learning Environment score task NLE staircase NLE staircase (level 3) NLE staircase (level 4) NLE oracle
Metrics: game score success rate sample efficiency behavior characteristics (gold, descents, kills)
Baselines: CDGPT5 RND E3B NovelD
Builds on: RLAIF Random Network Distillation PPO Sample Factory CDGPT5
Closest papers by meaning
- Ground-Compose-Reinforce: Grounding Language in Agentic Behaviours using Limited Data (NeurIPS 2025 · similarity 0.88)
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- Reward Hacking Benchmark: Measuring Exploits in LLM Agents with Tool Use (ICML 2026 · similarity 0.87)
- MaestroMotif: Skill Design from Artificial Intelligence Feedback (ICLR 2025 · similarity 0.87)
- Prompted Policy Search: Reinforcement Learning through Linguistic and Numerical Reasoning in LLMs (NeurIPS 2025 · similarity 0.87)
- Scaling Autonomous Agents via Automatic Reward Modeling And Planning (ICLR 2025 · similarity 0.87)
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