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Hypothesis, Verification, and Induction: Grounding Large Language Models with Self-Driven Skill Learning
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Proposes HYVIN, a Hypothesis-Verification-Induction-Deduction framework where an LLM decomposes BabyAI instructions into subgoals with check functions, RL verifies them, and clustered subgoals train reusable skills for LLM planning.
Problem
Grounding LLM plans in environments without hand-built low-level APIs or costly demonstrations, given sparse rewards and low generality of learned behaviors.
Approach
The LLM proposes subgoals and executable check functions that serve as intrinsic rewards for RL policies; verified subgoal trajectories are used to restore initial states, and semantically clustered subgoals train general skill policies that a few-shot LLM planner calls to write programs.
Key results
- On the hardest BabyAI BossLevel task, HYVIN reaches 75.9% success with zero demonstrations, versus 77% for the imitation-learning Original baseline trained on 1M demonstrations.
- HYVIN-no-skills reaches only 8.6% on BossLevel, and ChatGPT alone scores 0% on several levels, showing learned skills are necessary.
- On Crafter, HYVIN outperforms DreamerV2 on multi-step achievements such as 0 to stone pickaxe (32.5% vs 0.2%).
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| add learned skills to HYVIN (ablation) | HYVIN · BossLevel | success rate: 8.6 -> 75.9 | Helped |
| add learned skills instead of using primitive actions (ablation) | HYVIN · GoToLocal | success rate: 55.1 -> 99.9 | Helped |
| add learned skills instead of using primitive actions (ablation) | HYVIN · PickupLocal | success rate: 47.6 -> 92.9 | Helped |
| add learned skills instead of using primitive actions (ablation) | HYVIN · PutNextLocal | success rate: 0 -> 91.9 | Helped |
| restore the expected initial state using saved actions during skill training (ablation) | HYVIN skill learning · BabyAI skill training | learning efficiency | Helped |
| add repeated interaction debugging (ablation) | HYVIN high-level planner · BossLevel | success rate: more than 40% | Helped |
| add multiple attempts of stochastic skill policies (ablation) | HYVIN high-level planner · complex tasks | success rate | Helped |
| use GPT-4 instead of ChatGPT (ablation) | HYVIN · BossLevel | success rate: 75.9 -> 85.9 | Helped |
| use HYVIN instead of DreamerV2 | HYVIN · Crafter: 0 → wood pickaxe | success rate: 59.6% -> 90.6% | Helped |
| use HYVIN instead of DreamerV2 | HYVIN · Crafter: 0 → stone | success rate: 42.7% -> 31.4% | Hurt |
| use HYVIN instead of DreamerV2 | HYVIN · Crafter: 0 → stone pickaxe | success rate: 0.2% -> 32.5% | Helped |
| use HYVIN instead of the strongest listed imitation-learning baseline | HYVIN · GoToLocal | success rate: 99.8 -> 99.9 | No clear effect |
Surprising findings
- Using GPT-4 instead of ChatGPT raises BossLevel success from 75.9% to 85.9%.
- Restoring the initial state from successful trajectories affects skill learning efficiency; without it some skills could not be learned.
Limitations
- Single cycle of hypothesis-verification-induction; no multi-cycle learning.
- Only text-based instruction following is studied; multimodal LLMs left for future work.
- Direct decomposition fails on complex long-horizon tasks, and results depend on the chosen ChatGPT model.
Experimental setup
Datasets: BabyAI Crafter
Benchmarks: BabyAI Crafter
Metrics: success rate
Baselines: BabyAI Original (imitation learning) LID-Text LISA ChatGPT HYVIN-action HYVIN-no-skills DreamerV2
Builds on: Code as Policies SayCan BabyAI Voyager
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- Language-guided Skill Learning with Temporal Variational Inference (ICML 2024 · similarity 0.88)
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