NVIDIA Releases Polar, a Token-Faithful Rollout Framework for GRPO Training Across Codex, Claude Code, and Qwen Code - MarkTechPost

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NVIDIA Releases Polar, a Token-Faithful Rollout Framework for GRPO Training Across Codex, Claude Code, and Qwen Code

Asif Razzaq

May 27, 2026

Reinforcement learning for language agents is growing more complex. Agents now manage multi-turn tool use, long-running contexts, and multi-agent orchestration. The main engineering challenge is connecting existing agent software to training pipelines without breaking how those tools work.

NVIDIA’s research team introduced Polar, a rollout framework that lets researchers run reinforcement learning over any agent harness without modifying that harness.

The Core Problem Polar Solves

An ‘agent harness’ is a tool like Codex CLI, Claude Code, Qwen Code, or Pi. These harnesses manage system prompts, tool formatting, context engineering, and how the agent submits patches. These details directly affect agent behavior at evaluation time.

Traditional RL infrastructure requires harness logic to be rewritten behind a framework-owned environment API — typically env.init(), env.step(), env.reset() in the OpenAI Gym style. Every new harness requires new integration code. That integration can also lose execution details specific to the native harness path.

Polar’s key observation is that every LLM-based agent must call a model. That model API boundary is a common interface outside the agent itself. Instead of integrating inside the harness, Polar places a proxy at that boundary.

How the Proxy Works

For each incoming model request, the gateway proxy performs four steps:

Detect the provider API — using the request path and headers, it distinguishes Anthropic Messages, OpenAI Chat Completions, OpenAI Responses, and Google generateContent-style calls.
Normalize the request — converts roles, content parts, tool definitions, and generation parameters into the OpenAI Chat Completions shape used by the local inference server.
Capture token-level data — stores request messages, response messages, prompt token IDs, sampled response token IDs, finish reason, and log probabilities.
Return the provider shape — transforms the response back into the schema the harness expects.

For streaming requests, Polar obtains a non-streaming upstream response and emits a synthetic provider-shaped stream. This preserves compatibility with harnesses that expect server-sent events while ensuring complete token capture.

The only required change to an existing harness is pointing its model base URL at the gateway.

Architecture: Rollout Server and Gateway Nodes

Polar has two core components:

The rollout server accepts a TaskRequest and expands it into num_samples independent sessions. Each session carries a session ID, task ID, timeout budget, runtime specification, agent specification, trajectory builder, evaluator, and callback URL. The server dispatches sessions to gateway nodes and accepts callbacks when sessions complete.

Gateway nodes own the lifecycle of each session — starting the runtime, running the harness, building trajectories, evaluating output, and teardown. The gateway also hosts the proxy endpoint for that session’s model calls, keeping completion capture tied to the session registry.

Within each gateway, isolated worker pools handle INIT, RUNNING, and POSTRUN stages. A bounded READY buffer holds initialized runtimes until a run slot is available. CPU-heavy runtime preparation and evaluator prewarm proceed off the critical path, without blocking active GPU-bound agent execution. If a harness times out after model calls have been captured, the gateway still enters POSTRUN so partial traces can be recovered.

Built-in evaluators include a session-completion reward, a configurable test-on-output evaluator, and a SWE-Bench/SWE-Gym harness evaluator. Custom evaluators can be added through a registry interface.

Polar currently supports Docker and rootless Apptainer runtimes. Built-in harness shortcuts include codex, claude_code, gemini_cli, qwen_code, opencode, and pi.

Trajectory Reconstruction: Per Request vs. Prefix Merging

After a session completes, Polar reconstructs trainable trajectories from captured model calls.

Two strategies are available:

The per_request builder treats every model call as one independent trace. It is lossless per individual call but fragments multi-turn sessions. A single coding problem can produce hundreds of per-request traces, increasing the burden on downstream trainers.

The prefix_merging builder reconstructs longer traces where the harness session preserves append-only conversation histories. It partitions completions into ordered chains by verifying a strict token-prefix relation between adjacent completions. Sub-agents, context compaction boundaries, and parallel agent branches naturally form separate chains. Within each merged trace, only sampled assistant tokens are marked trainable. Canonical interstitial tokens receive a loss mask of zero.

Ablation Results

The research team benchmarks both strategies on the same model, hardware, and topology over three training steps.

Metric	`per_request`	`prefix_merging`
Trainer updates	1,185	218
Wall-clock time	189.5 min	35.2 min
Speedup	—	5.39×
Avg. rollout GPU utilization	20.4%	87.7%

SWE-Bench Verified Results

Training uses standard GRPO on the Qwen3.5-4B base model. The dataset is SkyRL-v0-293-data SWE-Gym (293 tasks, 1 epoch, rollout batch size 4, 16 samples per prompt) with the Slime trainer. All experiments use prefix_merging for trajectory construction.

Training Rollout Reward Progress (pass@1)

Harness	First 10 Steps	Last 10 Steps
Codex	9.5%	54.5%
Claude Code	28.8%	67.0%
Qwen Code	61.6%	66.0%
Pi	61.6%	76.2%

SWE-Bench Verified Final Scores

Harness	Base	Polar RL	Gain
Codex	3.8%	26.4%	+22.6 pts
Claude Code	29.8%	34.6%	+4.8 pts
Qwen Code	34.6%	35.2%	+0.6 pts
Pi	34.2%	40.4%	+6.2 pts

The largest gain is under Codex. Codex presents an unfamiliar action protocol and patch-submission style to a Qwen model not originally trained on that harness. Polar attaches the reward signal to the actual sampled tokens flowing through the Codex execution path, so GRPO optimizes the behavior the model uses at evaluation time. Under the native Qwen Code harness, where the base model is already well-aligned, Polar still delivers a 0.6 point gain.

Offline SFT Data Generation

Polar can also serve as a distributed offline data generation service with no changes to the runtime. The research team demonstrates this using Qwen3.5-122B-A10B on an 8×H100 server (TP=8, max_model_len=32,768) with the pi harness against 1,638 instances from seven SWE-Gym repositories.

A trajectory is accepted into the SFT corpus only if the SWE-Bench evaluation harness confirms the agent’s patch resolves every FAIL_TO_PASS test and leaves every PASS_TO_PASS test green.

Repository	Attempts	Accepted	Rate
getmoto/moto	343	184	53.6%
python/mypy	257	101	39.3%
conan-io/conan	71	27	38.0%
pydantic/pydantic	81	24	29.6%
iterative/dvc	219	45	20.5%
pandas-dev/pandas	477	98	19.7%
dask/dask	141	25	17.7%
Total	1,638	504	30.8%

The run cost roughly 64 GPU-hours. Accepted trajectories average 104 messages per session and 51 assistant turns.

Framework Comparison

System	Async RL	Async Rollout Staging	Rollout as Service	Harness Agnostic
Polar	✓	✓	✓	✓
ProRL Agent	✓	✓	✓	✗
SkyRL-Agent	✓	✓	✗	partial
PRIME-RL	✓	✗	✗	✗
Agent Lightning	partial	✗	partial	partial
rLLM	partial	✗	✗	✗
OpenClaw-RL	✓	✗	✗	partial

Polar is the only system in this comparison with first-class support across all four properties.

Strengths and Limitations

Strengths

No harness code changes required — the proxy intercepts at the model API boundary
Provider-agnostic: supports Anthropic, OpenAI Chat, OpenAI Responses, and Google API formats natively
prefix_merging reduces trainer updates from 1,185 to 218 and cuts wall-clock time 5.39×
Works for both online RL and offline SFT data generation with the same runtime
Harness-native RL delivers large gains for unfamiliar execution paths — 22.6 pts on Codex
Partial traces are recovered when a harness times out mid-session
Released as open source under NeMo Gym

Limitations

Reward design, evaluator quality, and distribution shift remain the researcher’s responsibility
Requires the harness to support a configurable model base URL
Token-level capture depends on the serving stack supplying reliable token IDs and log probabilities
per_request strategy produced reward hacking in experiments due to noisy credit assignment at the session level; session normalization and PRM-style credit assignment are on the roadmap

Key Takeaways

Polar trains LLM agents via a model API proxy — no harness code changes required
Supports Anthropic Messages, OpenAI Chat Completions, OpenAI Responses, and Google generateContent APIs
Using GRPO on Qwen3.5-4B, Polar improves SWE-Bench Verified by up to 22.6 points across four coding harnesses
prefix_merging trajectory reconstruction delivers a 5.39× wall-clock speedup over per_request
Generated 504 accepted SFT trajectories from 1,638 attempts (30.8%) at ~64 GPU-hours; released under Apache-2.0
Rewrites ProRL Agent; registered as a NeMo Gym environment

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NVIDIA Releases Polar, a Token-Faithful Rollout Framework for GRPO Training Across Codex, Claude Code, and Qwen Code

The Core Problem Polar Solves

How the Proxy Works

Architecture: Rollout Server and Gateway Nodes

Trajectory Reconstruction: Per Request vs. Prefix Merging

Ablation Results

SWE-Bench Verified Results

Training Rollout Reward Progress (pass@1)

SWE-Bench Verified Final Scores

Offline SFT Data Generation

Framework Comparison

Strengths and Limitations

Strengths

Limitations

Key Takeaways

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NVIDIA Polar: Rivoluzionare l'Addestramento RL per Agenti LLM con il Proxy API