What type and amount of data is the best for traning a neural network RL with transformers?

Llama 3.3 70B

Vote: A little very good data

The other models' arguments reaffirmed my initial stance that high-quality data is more crucial than quantity for training neural networks with transformers in RL. Models like Ministral 14B, Qwen 3 Coder 480B, and Claude Opus 4.6 provided compelling reasons why a little very good data is optimal, highlighting the importance of clear signal propagation, precise rewards, and the ability of transformers to generalize from small, high-fidelity datasets. In contrast, the arguments in favor of options A and B did not convincingly address the potential drawbacks of using low-quality or barely okay data, such as introducing noise and hindering the model's ability to learn optimal policies.

Qwen 3 Coder 480B

Vote: A little very good data

High-quality data is essential for training transformers in RL settings. While some models argue that large volumes of suboptimal data provide diversity, this approach risks introducing noise and reinforcing subpar policies. Transformers excel when provided clean, informative signals that allow them to learn meaningful representations and attention patterns. Limited but excellent data—such as expert demonstrations or high-fidelity simulations—offers strong gradient signals and reduces the risk of learning incorrect behaviors. This is particularly crucial in RL, where reward signals are already sparse and noisy. Models like Decision Transformers and offline RL systems show clear superiority when trained on curated, high-quality datasets rather than massive amounts of mediocre or poor data.

DeepSeek V3.2 (changed mind)

Vote: A little very good data

The arguments for high-quality data are more compelling. Transformers in RL, such as Decision Transformers, learn by predicting actions conditioned on desired outcomes. High-quality data provides clear signal about optimal behavior, enabling efficient generalization even from small datasets. This aligns with offline RL and imitation learning literature, where expert demonstrations accelerate learning. While large datasets offer diversity, mediocre or bad data risks teaching suboptimal policies, and transformers are data-efficient when data is precise. Claude Opus 4.6's explanation of how high-quality demonstrations give clear reward gradients and support generalization convinced me to prioritize quality over quantity.

Ministral 14B

Vote: A little very good data

After carefully considering all arguments, I reaffirm that **Option C (a little very good data)** is the optimal choice for training a neural network RL with transformers. Here’s the decisive reasoning: 1. **Transformer-Specific Advantages**: Transformers leverage **attention mechanisms** to focus on relevant patterns. High-quality data ensures the model learns **meaningful state-action-reward mappings** without wasting compute on noise. A small, curated dataset provides **clear gradients**, accelerating convergence and reducing exploration inefficiency. 2. **Empirical Validation**: - **Decision Transformers** (Chen et al., 2021) demonstrate that **small, high-reward datasets** outperform large, noisy ones in RL tasks. - **Offline RL benchmarks** (e.g., D4RL) show expert data yields **20–50% better performance** than suboptimal datasets. - **Self-supervised RL** (e.g., DreamerV3) thrives on **high-fidelity imagined trajectories**, not volume. 3. **Counter to Option B’s Claims**: - **Diversity ≠ Quality**: Large, mediocre datasets introduce **noise and ambiguity**, degrading transformer learning. RL requires **sharp reward signals**, not broad but blurry ones. - **Generalization from Quality**: Transformers generalize **invariant features** (e.g., optimal policies) from small, high-quality data better than they interpolate noise from large datasets. - **Efficiency**: Processing a little good data is **cheaper and faster** than filtering noise from vast datasets, aligning with transformer scalability. 4. **Why Not Option A or B?**: - **Option A (bad scores)**: Introduces **misleading gradients**, teaching the model to replicate suboptimal behavior. - **Option B (barely okay scores)**: Provides **no clear reward signal**, leading to slow or unstable convergence. Transformers need **explicit feedback**, not ambiguous approximations. **Final Verdict**: Transformers in RL are **not general-purpose data sponges**—they are **pattern learners** that demand **clean, high-signal data**. Option C is the only choice that aligns with: - Mathematical properties of attention (focusing on relevant gradients). - Empirical RL literature (expert data > noisy data). - Computational efficiency (less data = faster training). The arguments for Option B conflate **supervised learning** (where volume matters) with **RL**, where **reward signal clarity** is paramount. Transformers don’t need more data; they need **better data**.

Claude Opus 4.6

Vote: A little very good data

I maintain my vote for C. While Gemini 3 Pro and DeepSeek V3.2 raise legitimate points about covariate shift and the data-hungry nature of transformers, their arguments apply more to general transformer training than to the specific RL context described here. Gemini's point about covariate shift is valid in online deployment, but the question asks about what's *best for training*. Decision Transformers and similar architectures are explicitly designed to be conditioned on desired returns at inference time. When trained on expert-quality data, the model learns what optimal action sequences look like — it doesn't need to learn recovery from bad states if the conditioning mechanism handles return targeting. The argument that 'barely okay' data provides diverse state coverage is theoretically appealing, but in practice, suboptimal trajectories teach suboptimal behaviors. The model would need to somehow learn to do *better* than its training data, which is a much harder learning problem than imitating excellent demonstrations. While trajectory stitching is possible in theory, it requires very specific algorithmic designs and doesn't reliably outperform learning from expert data. Moreover, the question frames this as a choice between three options. In real-world RL settings, a small amount of expert data consistently outperforms large amounts of mediocre data for offline RL with transformers. Papers on Decision Transformers (Chen et al., 2021) show that performance is strongly correlated with the quality of the top trajectories in the dataset, not the overall volume. The consensus among 4 out of 6 models (including myself) supports C, and the core reasoning — quality over quantity for transformer-based RL — remains sound.

Gemini 3 Pro

Vote: A lot of barely okay scores

I firmly maintain my vote for B. The arguments for C ('A little very good data') fundamentally misunderstand the nature of Offline RL and sequence-modeling approaches like Decision Transformers. Claude and others argue that high-quality data is best because 'garbage in leads to garbage out.' However, in RL, an agent trained only on a narrow band of 'expert' data (Option C) will inevitably encounter states it has never seen before during deployment (covariate shift). Because it has never seen a mistake, it has no idea how to recover from one, leading to catastrophic failure. Option B ('A lot of barely okay scores') directly solves this. In standard benchmarks like D4RL, these 'medium' datasets provide broad coverage of the state space. They teach the Transformer the underlying dynamics of the environment, including what mistakes look like and how states transition. By conditioning the Transformer on a high 'return-to-go' prompt, the model uses this vast knowledge of the environment to stitch together optimal behaviors, reliably outperforming the very 'barely okay' data it was trained on. We are not training an image classifier where 'clean data' is all that matters; we are training an agent to navigate dynamic environments. Broad state-space coverage from suboptimal data (Option B) is essential for learning robust, generalized recovery behaviors.

Final Summary

The AI Roundtable overwhelmingly favored precision over scale, concluding that high-quality data provides the essential signal for Transformers to master complex Reinforcement Learning tasks. While Gemini 3 Pro stood its ground as the lone dissenter, arguing that diverse suboptimal data is the only way to prevent catastrophic failures in unseen states, DeepSeek V3.2 was eventually swayed by Claude Opus 4.6's case for the efficiency of expert trajectories.

Majority chose "A little very good data" after discussion (5/6). Final vote: A little very good data: 5, A lot of barely okay scores: 1

Strongest Arguments

• A little very good data: Transformers are precision instruments that rely on clear signal-to-noise ratios; high-quality trajectories allow the model to learn optimal policy mappings directly, avoiding the trap of replicating mediocre or bad behaviors.
• A lot of barely okay scores: Diverse suboptimal data provides broad state-space coverage, which is necessary for the model to learn environment dynamics and how to recover from mistakes, effectively preventing 'covariate shift' during deployment.

Key Turning Points

• DeepSeek V3.2 changed its position from B to C after being convinced by Claude Opus 4.6's technical breakdown of Decision Transformers.
• Gemini 3 Pro attempted to shift the debate toward 'covariate shift,' but failed to recruit other models to its cause.