HODL Benchmark: Yann LeCun's Challenge for True Long-Horizon Reasoning in LLMs

## The Quest for Genuine AI Reasoning In the fast-evolving world of artificial intelligence, large language models (LLMs) have dazzled us with their ability to generate text, solve puzzles, and even code. Yet, a nagging question persists: Do these models truly *reason*, especially when faced with complex, multi-step problems that unfold over dozens or hundreds of actions? Traditional benchmarks like ARC, MATH, or GPQA often test isolated skills—short bursts of logic or math prowess—but they fall short in capturing the essence of sustained, long-horizon reasoning. This is where Yann LeCun, Meta's Chief AI Scientist, steps in with HODL: the Holistic Evaluation Of Reasoning moDels. HODL isn't just another leaderboard; it's a rigorous framework designed to probe how well LLMs plan, adapt, and execute over extended horizons. Drawing from classic AI planning domains, it generates an endless stream of procedurally created puzzles, ensuring models can't memorize solutions. Released via [the official GitHub repository](https://github.com/facebookresearch/holistic-evaluation-of-reasoning-models), HODL reveals a stark truth: even frontier models are nowhere near human-level performance on these tasks. ## Why Long-Horizon Reasoning Matters Imagine stacking blocks into a pyramid, navigating disks across pegs in Tower of Hanoi, or ferrying animals across a river without mishaps—all while receiving noisy, partial observations. These aren't abstract thought experiments; they're proxies for real-world applications like robotics, game AI, or autonomous systems, where decisions compound over time. Current LLMs excel at one-shot answers but falter in iterative, stateful environments. They might ace a single math equation but crumble when asked to maintain a mental model across 50+ steps. HODL addresses this by emphasizing: - **Procedural generation**: Infinite, unique instances prevent data contamination. - **Long sequences**: Problems scale to 20-100 actions, testing memory and foresight. - **Noisy observations**: Realistic partial info mimics sensor data in the physical world. - **Zero-shot evaluation**: Models get task descriptions only—no examples or fine-tuning. Yann LeCun highlighted this gap in a viral tweet, showcasing how even advanced models like GPT-4o score dismally, underscoring that LLMs are pattern-matchers, not reasoners. ## Diving into HODL's Five Benchmark Tasks HODL comprises five diverse tasks, each rooted in established planning literature but adapted for LLM evaluation. They progressively ramp up complexity, from pure planning to interference-heavy scenarios. Let's explore each with practical insights. ### 1. Blocksworld IIA: Mastering Spatial Planning This task draws from the Blocks World domain, a staple in AI planning since the 1970s. The goal? Rearrange stacks of colored blocks into target configurations using a robot arm, given observations like "Red is on Blue, Green is on Table." Key features: - **Scale**: Up to 50 actions, with 5-10 blocks. - **Observations**: Natural language descriptions of the current state (e.g., "The blue block is on top of the red block on the table.") - **Actions**: Commands like `pickup(block)`, `putdown(block)`, `stack(block1, block2)`. **Real-world analogy**: Robotic assembly lines, where grippers manipulate parts amid clutter. Example interaction: ``` Observation: Yellow on table, Red on table, Blue on Yellow. Target: Red on Blue on table, Yellow on table. Action: pickup(Yellow) New Observation: Red on table, Blue on Yellow (held), ... ``` Models must track hidden states—no full world model provided each step. ### 2. Tower of Hanoi: Precision in Recursive Moves The iconic puzzle: Move a tower of disks from peg A to peg C, using peg B as auxiliary, never placing a larger disk on a smaller one. HODL variant: - **Disks**: 3 to 10 (10 requires 1023 moves!). - **Observations**: Peg contents described linguistically. - **Challenge**: Exponential state space tests foresight. **Pro tip for evaluation**: Smaller instances (3-5 disks) serve as sanity checks; scaling reveals planning limits. Human solvers use recursion; LLMs often loop or hallucinate invalid moves. ### 3. River Crossing Puzzles: Constraint Satisfaction Over Time Variants of the classic wolf-goat-cabbage riddle, scaled up: - **Agents**: Up to 5 entities (e.g., fox, goose, corn, farmer). - **Rules**: Prevent invalid pairs (fox eats goose, etc.) when unattended. - **Boat capacity**: 1-2 passengers. Observations track positions on left/right banks and boat. **Application**: Logistics scheduling, like drone deliveries avoiding conflicts. Example state: ``` Left bank: Farmer, Fox, Goose Right bank: Corn Boat: empty Action: farmer takes goose to right ``` ### 4. Blocksworld with Interference: Robustness Under Adversity An extension of Blocksworld IIA, but with a twist: An adversarial agent randomly moves blocks between your turns. - **Interference rate**: 20-50% of actions disrupted. - **Resilience test**: Models must replan on-the-fly. This mirrors dynamic environments like multi-robot warehouses (e.g., Amazon Robotics). ### 5. Longest Increasing Subsequence (LIS): Sequential Decision-Making Given a permutation of numbers 1-N, output the length of the longest strictly increasing subsequence via queries. - **Queries**: Ask for element at position i. - **Output**: Just the length—no sequence needed. - **Horizon**: Up to 100 elements. **Why clever?** Forces exploration strategy; brute-force querying fails for large N. Optimal humans use patience sorting; LLMs guess poorly. ## HODL Leaderboard: The Harsh Reality Check Evaluations use few-shot prompting (3 examples per task) in text-only mode. Results as of launch: | Model | Blocksworld IIA | Hanoi | River | Interference | LIS | **Average** | |--------------------|-----------------|-------|-------|--------------|------|--------------| | o1-preview | 28.6% | 36.8% | 22.5% | 15.4% | 29.1%| **26.5%** | | o1-mini | 24.3% | 31.2% | 18.9% | 12.7% | 25.6%| **22.5%** | | Claude 3.5 Sonnet | 12.1% | 14.5% | 9.8% | 7.2% | 18.3%| **12.4%** | | GPT-4o | 8.7% | 11.2% | 7.5% | 5.9% | 14.2%| **9.5%** | | Llama 3.1 405B | 6.4% | 8.9% | 5.6% | 4.1% | 11.7%| **7.3%** | Humans? Easily 90%+ on most. o1's chain-of-thought shines but plateaus—hinting architectural limits. ## Hands-On: Running HODL Evaluations Yourself Getting started is straightforward, thanks to the [HODL GitHub repo](https://github.com/facebookresearch/holistic-evaluation-of-reasoning-models). ```bash pip install hodl-benchmark ``` Evaluate a model: ```python import hodl results = hodl.eval_model( model="anthropic/claude-3-5-sonnet-20240620", tasks=["blocksworld_iia", "hanoi", "river_crossing", "blocksworld_interference", "lis"], max_steps=100, n_instances=50 ) print(results) ``` Customize: - `temperature=0` for determinism. - Scale `n_instances` for stats. - Integrate with OpenAI/Anthropic APIs. Submit to the leaderboard via pull request—contribute to AI progress! ## Broader Implications and the Path Forward HODL spotlights key bottlenecks: Working memory limits, planning horizon, and robustness to noise. It's a call to action for: - **Hybrid architectures**: Combine LLMs with symbolic planners (e.g., Monte Carlo Tree Search). - **Long-context training**: Models like Gemini 1.5 push boundaries, but reasoning lags. - **Multimodal extensions**: Imagine visual Blocksworld for embodied AI. Yann LeCun envisions HODL evolving into robotics benchmarks. For developers, it's actionable: Benchmark your fine-tunes, iterate prompts (e.g., "Think step-by-step, track state explicitly"), or build agents atop these tasks. In summary, HODL isn't doom-and-gloom—it's a milestone. By quantifying the reasoning gap, it guides us toward AI that doesn't just predict tokens but truly understands and acts in complex worlds. --- <div style="text-align: center; margin-top: 2rem;"> <a href="https://www.deeplearning.ai/blog/hodl-yann-lecun/" target="_blank" rel="noopener noreferrer" class="view-full-resource-btn" style="display: inline-block; background-color: #f97316; color: white; padding: 12px 24px; border-radius: 8px; text-decoration: none; font-weight: 600; transition: background-color 0.2s;">View Full Resource</a> </div>