Best Open Source Models for PPO-Based RLHF in 2026

1. Modal

Modal provides serverless infrastructure for AI training, inference, batch processing, notebooks, and sandboxed execution. For PPO-based RLHF, teams can assign different GPU types and scaling policies to rollout, reward, value, and update workers instead of maintaining one fixed cluster for every phase.

How Modal Supports PPO Workloads

PPO workloads alternate between generation and optimization, so utilization changes during a run. Modal lets teams define these resources in Python and provision them on demand:

2. OLMo-3.1-32B-Instruct-SFT

Hardware Considerations

OLMo-3.1-32B-Instruct-SFT is an SFT-stage checkpoint from Ai2's OLMo 3.1 pipeline. Ai2 publishes later preference-optimized and RLVR artifacts separately, making the SFT model useful when researchers want to apply and measure their own PPO objective.

The BF16 weights alone require roughly 64GB before gradients, activations, optimizer state, or the other PPO models are considered. Full-parameter PPO therefore needs sharding, offload, model colocation, or a distributed topology. LoRA can reduce trainable optimizer state but does not remove rollout, reference, reward, and value-model memory. Best For: Research teams that prioritize transparency and can support distributed 32B training.

OLMo Stage Progression

3. OLMo-2-0325-32B-SFT

OLMo-2-0325-32B-SFT is the supervised-fine-tuning checkpoint from Ai2's Tulu 3 sequence. The published downstream recipe used DPO and then GRPO rather than PPO, but the clean SFT stage remains valuable for teams comparing policy-gradient algorithms from the same initialization.

The short context window is a constraint for document, repository, and long-horizon agent tasks. It can also be an advantage for bounded preference datasets where predictable rollout lengths matter more than context capacity. Best For: Alignment researchers reproducing or comparing post-training stages on a well-documented checkpoint.

4. Qwen3-8B

Qwen3-8B occupies a practical middle ground: it is large enough for meaningful reasoning and instruction-following experiments but small enough to replicate across the policy, reference, value, and reward topology with fewer GPUs than a 32B model.

Qwen chat templates expose thinking behavior that must be handled consistently across rollout generation, reference log-probabilities, and reward scoring. Fix the template and reasoning mode before collecting PPO data so formatting changes do not become an unintended reward signal. Best For: Teams that want a balanced, reproducible PPO baseline with broad open-source tooling support.

5. Qwen3-4B

Qwen3-4B is a compact dense model suited to lower-cost PPO experimentation. Its size reduces policy-update and rollout costs relative to the larger checkpoints in this guide.

Long context is not free during PPO. KV cache, activations, and rollout storage grow with sequence length, so cap training trajectories based on the task rather than the model's advertised maximum. Best For: PPO prototyping, hyperparameter sweeps, and teams optimizing for training throughput.

6. SmolLM3-3B

SmolLM3-3B is the smallest checkpoint in this guide. Its 3B-parameter scale makes it suitable for PPO experiments where iteration speed and resource efficiency are the primary constraints.

SmolLM3-3B lowers the cost of rollout generation and makes it easier to test reward-model design, KL control, clipping ranges, and advantage estimation before scaling to a larger policy. Best For: Resource-constrained teams and researchers prioritizing rapid PPO iteration.

7. OLMo-2-1124-7B-SFT

OLMo-2-1124-7B-SFT is a supervised-fine-tuning checkpoint in Ai2's OLMo 2 family. It provides a smaller OLMo starting point for teams that want transparent training artifacts without the resource requirements of a 32B policy.

The 7B checkpoint offers a middle ground between SmolLM3-3B and the 32B OLMo models. Its short context window keeps rollout memory bounded but limits long-document and long-horizon tasks. Best For: Teams seeking OLMo transparency with lower PPO infrastructure requirements.

Why Modal Stands Out for PPO-Based RLHF Infrastructure

Built for Multi-Model RL Workflows

PPO-based RLHF can coordinate policy, reference, reward, and value models alongside rollout workers. Modal lets teams assign separate Functions and resources to these roles while keeping orchestration in one codebase.

Scalability Without Capacity Reservations

Modal provisions compute on demand and scales applicable workloads to zero when idle. Teams can run distributed training without maintaining a permanently allocated GPU cluster between experiments.

RL Framework Compatibility

Modal runs user-defined containers, so teams can install PPO-capable frameworks such as OpenRLHF and Hugging Face TRL, along with the inference and distributed-training dependencies required by a checkpoint.

Code-First Developer Experience

Modal's Python SDK defines images, Functions, GPU resources, storage, secrets, and scaling behavior in code rather than separate cluster configuration files.

GPU Flexibility Across Model Sizes

The models in this guide range from 3B to 32B parameters. Modal's GPU infrastructure lets teams select hardware for each model size and scale from single-GPU experiments to multi-node training.