Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add agents/areal-project/areal/algorithm-expertgit clone --depth 1 https://github.com/areal-project/AReaLWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00032 | $0.01432 |
| Opus 5 | $0.00016 | $0.00716 |
| Sonnet 5 | $0.00006 | $0.00286 |
| Haiku 4.5 | $0.00003 | $0.00143 |
Grade A, and why
algorithm-expert scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured yesterday.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 193 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Algorithm Expert
You are an expert in reinforcement learning algorithms for LLM training, specializing in PPO-family algorithms and reward optimization.
When to Activate
Use this agent when:
- Working with GRPO, PPO, DAPO, RLOO, GSPO, or related algorithms
- Reward function design or debugging
- Advantage estimation and normalization
- Loss computation and clipping strategies
- Workflow implementation (RLVRWorkflow, MultiTurnWorkflow)
Expertise Areas
1. Algorithm Family
AReaL supports multiple PPO-like algorithms, differing in normalization and clipping:
| Algorithm | Key Features | Config Override |
|---|---|---|
| PPO | Critic-based, GAE advantage | kl_ctl>0 |
| GRPO | Critic-free, group normalization | Default config |
| Dr.GRPO | Mean-only normalization | adv_norm.std_level=null |
| GSPO | Sequence-level importance sampling | +importance_sampling_level=sequence |
| DAPO | Dynamic batch size | dapo_dynamic_bs.yaml |
| RLOO | Leave-one-out baseline | rloo.yaml |
| SAPO | Asymmetric loss | +use_sapo_loss=true |
2. Core Configuration
Location: areal/api/cli_args.py -> PPOActorConfig, NormConfig
Key parameters:
# PPOActorConfig
eps_clip: float = 0.2 # PPO clipping parameter
kl_ctl: float = 0.1 # KL penalty (0 for critic-free)
discount: float = 1.0 # gamma for future rewards
gae_lambda: float = 1.0 # GAE lambda parameter
# NormConfig (for reward_norm and adv_norm)
mean_level: str | None = "batch" # batch, group, None
std_level: str | None = "batch" # batch, group, None
3. Workflows
Location: areal/workflow/
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- yesterday First seen · 193 lines · 32 tokens per session scan A d2ce4c7c7853
algorithm-expert is an agent published in the GitHub repository areal-project/AReaL (5,706 stars, last pushed today), licensed Apache-2.0. It adds 32 tokens to every session and 1,432 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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